## Part 1: The basics

### Introduction

This is the first in a series of posts on MCMC-based fully Bayesian inference for a logistic regression model. In this series we will look at the model, and see how the posterior distribution can be sampled using a variety of different programming languages and libraries.

## Logistic regression

Logistic regression is concerned with predicting a binary outcome based on some covariate information. The probability of "success" is modelled via a logistic transformation of a linear predictor constructed from the covariate vector.

This is a very simple model, but is a convenient toy example since it is arguably the simplest interesting example of an intractable (nonlinear) statistical model requiring some kind of iterative numerical fitting method, even in the non-Bayesian setting. In a Bayesian context, the posterior distribution is intractable, necessitating either approximate or computationally intensive numerical methods of "solution". In this series of posts, we will mainly concentrate on MCMC algortithms for sampling the full posterior distribution of the model parameters given some observed data.

We assume $n$ observations and $p$ covariates (including an intercept term that is always 1). The binary observations $y_i,\ i=1,\ldots,n$ are 1 for a "success" and 0 for a "failure". The covariate $p$-vectors $x_i, i=1,\ldots,n$ all have 1 as the first element. The statistical model is

$\displaystyle \text{logit}\left(\text{Pr}[Y_i = 1]\right) = x_i \cdot \beta,\quad i=1,\ldots,n,$

where $\beta$ is a $p$-vector of parameters, $a\cdot b = a^\textsf{T} b$, and

$\displaystyle \text{logit}(q) \equiv \log\left(\frac{q}{1-q}\right),\quad \forall\ q\in (0,1).$

Equivalently,

$\displaystyle \text{Pr}[Y_i = 1] = \text{expit}(x_i \cdot \beta),\quad i=1,\ldots,n,$

where

$\displaystyle \text{expit}(\theta) \equiv \frac{1}{1+e^{-\theta}},\quad \forall\ \theta\in\mathbb{R}.$

Note that the expit function is sometimes called the logistic or sigmoid function, but expit is slightly less ambiguous. The statistical problem is to choose the parameter vector $\beta$ to provide the "best" model for the probability of success. In the Bayesian setting, a prior distribution (typically multivariate normal) is specified for $\beta$, and then the posterior distribution after conditioning on the data is the object of inferential interest.

### Example problem

In order to illustrate the ideas, it is useful to have a small running example. Here we will use the (infamous) Pima training dataset (MASS::Pima.tr in R). Here there are $n=200$ observations and 7 predictors. Adding an intercept gives $p=8$ covariates. For the Bayesian analysis, we need a prior on $\beta$. We will assume independent mean zero normal distributions for each component. The prior standard deviation for the intercept will be 10 and for the other covariates will be 1.

### Describing the model in some PPLs

In this first post in the series, we will use probabilistic programming languages (PPLs) to describe the model and sample the posterior distribution.

#### JAGS

JAGS is a stand-alone domain specific language (DSL) for probabilistic programming. It can be used independently of general purpose programming languages, or called from popular languages for data science such as Python and R. We can describe our model in JAGS with the following code.

  model {
for (i in 1:n) {
y[i] ~ dbern(pr[i])
logit(pr[i]) <- inprod(X[i,], beta)
}
beta[1] ~ dnorm(0, 0.01)
for (j in 2:p) {
beta[j] ~ dnorm(0, 1)
}
}


Note that JAGS uses precision as the second parameter of a normal distribution. See the full runnable R script for further details. Given this model description, JAGS can construct an MCMC sampler for the posterior distribution of the model parameters given the data. See the full script for how to feed in the data, run the sampler, and analyse the output.

#### Stan

Stan is another stand-alone DSL for probabilistic programming, and has a very sophisticated sampling algorithm, making it a popular choice for non-trivial models. It uses gradient information for sampling, and therefore requires a differentiable log-posterior. We could encode our logistic regression model as follows.

data {
int<lower=1> n;
int<lower=1> p;
int<lower=0, upper=1> y[n];
real X[n,p];
}
parameters {
real beta[p];
}
model {
for (i in 1:n) {
real eta = dot_product(beta, X[i,]);
real pr = 1/(1+exp(-eta));
y[i] ~ binomial(1, pr);
}
beta[1] ~ normal(0, 10);
for (j in 2:p) {
beta[j] ~ normal(0, 1);
}
}


Note that Stan uses standard deviation as the second parameter of the normal distribution. See the full runnable R script for further details.

#### PyMC

JAGS and Stan are stand-alone DSLs for probabilistic programming. This has the advantage of making them independent of any particular host (general purpose) programming language. But it also means that they are not able to take advantage of the language and tool support of an existing programming language. An alternative to stand-alone DSLs are embedded DSLs (eDSLs). Here, a DSL is embedded as a library or package within an existing (general purpose) programming language. Then, ideally, in the context of PPLs, probabilistic programs can become ordinary values within the host language, and this can have some advantages, especially if the host language is sophisticated. A number of probabilistic programming languages have been implemented as eDSLs in Python. Python is not a particularly sophisticated language, so the advantages here are limited, but not negligible.

PyMC is probably the most popular eDSL PPL in Python. We can encode our model in PyMC as follows.

pscale = np.array([10.,1.,1.,1.,1.,1.,1.,1.])
with pm.Model() as model:
beta = pm.Normal('beta', 0, pscale, shape=p)
eta = pmm.matrix_dot(X, beta)
pm.Bernoulli('y', logit_p=eta, observed=y)
traces = pm.sample(2500, tune=1000, init="adapt_diag", return_inferencedata=True)


See the full runnable script for further details.

#### NumPyro

NumPyro is a fork of Pyro for NumPy and JAX (of which more later). We can encode our model with NumPyro as follows.

pscale = jnp.array([10.,1.,1.,1.,1.,1.,1.,1.]).astype(jnp.float32)
def model(X, y):
coefs = numpyro.sample("beta", dist.Normal(jnp.zeros(p), pscale))
logits = jnp.dot(X, coefs)
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=y)


See the full runnable script for further details.

Please note that none of the above examples have been optimised, or are even necessarily expressed idiomatically within each PPL. I’ve just tried to express the model in as simple and similar way across each PPL. For example, I know that there is a function bernoulli_logit_glm in Stan which would simplify the model and improve sampling efficiency, but I’ve deliberately not used it in order to try and keep the implementations as basic as possible. The same will be true for all of the code examples in this series of blog posts. The code has not been optimised and should therefore not be used for serious benchmarking.

### Next steps

PPLs are convenient, and are becoming increasingly sophisticated. Each of the above PPLs provides a simple way to pass in observed data, and automatically construct an MCMC algorithm for sampling from the implied posterior distribution – see the full scripts for details. All of the PPLs work well for this problem, and all produce identical results up to Monte Carlo error. Each PPL has its own approach to sampler construction, and some PPLs offer multiple choices. However, more challenging problems often require highly customised samplers. Such samplers will often need to be created from scratch, and will require (at least) the ability to compute the (unnormalised log) posterior density at proposed parameter values, so in the next post we will look at how this can be derived for this model (in a couple of different ways) and coded up from scratch in a variety of programming languages.

All of the complete, runnable code associated with this series of blog posts can be obtained from this public github repo.

## Unbiased MCMC with couplings

Yesterday there was an RSS Read Paper meeting for the paper Unbiased Markov chain Monte Carlo with couplings by Pierre Jacob, John O’Leary and Yves F. Atchadé. The paper addresses the bias in MCMC estimates due to lack of convergence to equilibrium (the “burn-in” problem), and shows how it is possible to modify MCMC algorithms in order to construct estimates which exactly remove this bias. The requirement is to couple a pair of MCMC chains so that they will at some point meet exactly and thereafter remain coupled. This turns out to be easier to do that one might naively expect. There are many reasons why we might want to remove bias from MCMC estimates, but the primary motivation in the paper was the application to parallel MCMC computation. The idea here is that many pairs of chains can be run independently on any available processors, and the unbiased estimates from the different pairs can be safely averaged to get an (improved) unbiased estimate based on all of the chains. As a discussant of the paper, I’ve spent a bit of time thinking about this idea, and have created a small repository of materials relating to the paper which may be useful for others interested in understanding the method and how to use it in practice.

The repo includes a page of links to related papers, blog posts, software and other resources relating to unbiased MCMC that I’ve noticed on-line.

Earlier in the year I gave an internal seminar at Newcastle giving a tutorial introduction to the main ideas from the paper, including runnable R code implementations of the examples. The talk was prepared as an executable R Markdown document. The R Markdown source code is available in the repo, but for the convenience of casual browsers I’ve also included a pre-built set of PDF slides. Code examples include code for maximal coupling of two (univariate) distributions, coupling Metropolis-Hastings chains, and coupling a Gibbs sampler for an AR(1) process.

I haven’t yet finalised my written discussion contribution, but the slides I presented at the Read Paper meeting are also available. Again, there is source code and pre-built PDF slides. My discussion focused on seeing how well the technique works for Gibbs samplers applied to high-dimensional latent process models (an AR(1) process and a Gaussian Markov random field), and reflecting on the extent to which the technique really solves the burn-in/parallel MCMC problem.

The repo also contains a few stand-alone code examples. There are some simple tutorial examples in R (largely derived from my tutorial introduction), including implementation of (univariate) independent and reflection maximal couplings, and a coupled AR(1) process example.

The more substantial example concerns a coupled Gibbs sampler for a GMRF. This example is written in the Scala programming language. There are a couple of notable features of this implementation. First, the code illustrates monadic coupling of probability distributions, based on the Rand type in the Breeze scientific library. This provides an elegant way to max couple arbitrary (continuous) random variables, and to create coupled Metropolis(-Hastings) kernels. For example, a coupling of two distributions can be constructed as

  def couple[T](p: ContinuousDistr[T], q: ContinuousDistr[T]): Rand[(T, T)] = {
def ys: Rand[T] =
for {
y  <- q
w  <- Uniform(0, 1)
ay <- if (math.log(w) > p.logPdf(y) - q.logPdf(y)) Rand.always(y) else ys
} yield ay
val pair = for {
x <- p
w <- Uniform(0, 1)
} yield (math.log(w) <= q.logPdf(x) - p.logPdf(x), x)
pair flatMap {
case (b, x) => if (b) Rand.always((x, x)) else (ys map (y => (x, y)))
}
}


and then draws can be sampled from the resulting Rand[(T, T)] polymorphic type as required. Incidentally, this also illustrates how to construct an independent maximal coupling without evaluating any raw likelihoods.
The other notable feature of the code is the use of a parallel comonadic image type for parallel Gibbs sampling of the GMRF, producing a (lazy) Stream of coupled MCMC samples.

## Background

In May I attended a great workshop on advances and challenges in machine learning languages at the CMS in Cambridge. There was an a good mix of people from different disciplines, and a bit of a theme around probabilistic programming. The workshop schedule includes links to many of the presentations, and is generally worth browsing. In particular, it includes a link to the slides for my presentation on a compositional approach to scalable Bayesian computation and probabilistic programming. I’ve given a few talks on this kind of thing over the last couple of years, at Newcastle, at the Isaac Newton Institute in Cambridge (twice), and at CIRM in France. But I think I explained things best at this workshop at the CMS, though my impression could partly have been a reflection of the more interested and relevant audience. In the talk I started with a basic explanation of why ideas from category theory and functional programming can help to solve problems in statistical computing in a more composable and scalable way, before moving on to discuss probability monads and their fundamental connection to probabilistic programming. The take home message from the talk is that if you have a generic inference algorithm, expressing the logic in the context of probability monads can give you an embedded probabilistic programming language (PPL) for that inference algorithm essentially “for free”.

So, during my talk I said something a little fool-hardy. I can’t remember my exact words, but while presenting the idea behind an SMC-based probability monad I said something along the lines of “one day I will write a blog post on how to write a probabilistic programming language from scratch in 50 lines of code, and this is how I’ll do it“! Rather predictably (with hindsight), immediately after my talk about half a dozen people all pleaded with me to urgently write the post! I’ve been a little busy since then, but now that things have settled down a little for the summer, I’ve some time to think and code, so here is that post.

## Introduction

The idea behind this post is to show that, if you think about the problem in the right way, and use a programming language with syntactic support for monadic composition, then producing a flexible, general, compositional, embedded domain specific language (DSL) for probabilistic programming based on a given generic inference algorithm is no more effort than hard-coding two or three illustrative examples. You would need to code up two or three examples for a paper anyway, but providing a PPL is way more useful. There is also an interesting converse to this, which is that if you can’t easily produce a PPL for your “general” inference algorithm, then perhaps it isn’t quite as “general” as you thought. I’ll try to resist exploring that here…

To illustrate these principles I want to develop a fairly minimal PPL, so that the complexities of the inference algorithm don’t hide the simplicity of the PPL embedding. Importance sampling with resampling is probably the simplest useful generic Bayesian inference algorithm to implement, so that’s what I’ll use. Note that there are many limitations of the approach that I will adopt, which will make it completely unsuitable for “real” problems. In particular, this implementation is: inefficient, in terms of both compute time and memory usage, statistically inefficient for deep nesting and repeated conditioning, due to the particle degeneracy problem, specific to a particular probability monad, strictly evaluated, impure (due to mutation of global random number state), etc. All of these things are easily fixed, but all at the expense of greater abstraction, complexity and lines of code. I’ll probably discuss some of these generalisations and improvements in future posts, but for this post I want to keep everything as short and simple as practical. It’s also worth mentioning that there is nothing particularly original here. Many people have written about monadic embedded PPLs, and several have used an SMC-based monad for illustration. I’ll give some pointers to useful further reading at the end.

## The language, in 50 lines of code

Without further ado, let’s just write the PPL. I’m using plain Scala, with just a dependency on the Breeze scientific library, which I’m going to use for simulating random numbers from standard distributions, and evaluation of their log densities. I have a directory of materials associated with this post in a git repo. This post is derived from an executable tut document (so you know it works), which can be found here. If you just want to follow along copying code at the command prompt, just run sbt from an empty or temp directory, and copy the following to spin up a Scala console with the Breeze dependency:

set libraryDependencies += "org.scalanlp" %% "breeze" % "1.0-RC4"
set libraryDependencies += "org.scalanlp" %% "breeze-natives" % "1.0-RC4"
set scalaVersion := "2.13.0"
console


import breeze.stats.{distributions => bdist}
import breeze.linalg.DenseVector


which are not strictly necessary, but clean up the subsequent code. We are going to use a set of weighted particles to represent a probability distribution empirically, so we’ll start by defining an appropriate ADT for these:

implicit val numParticles = 300

case class Particle[T](v: T, lw: Double) { // value and log-weight
def map[S](f: T => S): Particle[S] = Particle(f(v), lw)
}


We also include a map method for pushing a particle through a transformation, and a default number of particles for sampling and resampling. 300 particles are enough for illustrative purposes. Ideally it would be good to increase this for more realistic experiments. We can use this particle type to build our main probability monad as follows.

trait Prob[T] {
val particles: Vector[Particle[T]]
def map[S](f: T => S): Prob[S] = Empirical(particles map (_ map f))
def flatMap[S](f: T => Prob[S]): Prob[S] = {
Empirical((particles map (p => {
f(p.v).particles.map(psi => Particle(psi.v, p.lw + psi.lw))
})).flatten).resample
}
def resample(implicit N: Int): Prob[T] = {
val lw = particles map (_.lw)
val mx = lw reduce (math.max(_,_))
val rw = lw map (lwi => math.exp(lwi - mx))
val law = mx + math.log(rw.sum/(rw.length))
val ind = bdist.Multinomial(DenseVector(rw.toArray)).sample(N)
val newParticles = ind map (i => particles(i))
Empirical(newParticles.toVector map (pi => Particle(pi.v, law)))
}
def cond(ll: T => Double): Prob[T] =
Empirical(particles map (p => Particle(p.v, p.lw + ll(p.v))))
def empirical: Vector[T] = resample.particles.map(_.v)
}

case class Empirical[T](particles: Vector[Particle[T]]) extends Prob[T]


Note that if you are pasting into the Scala REPL you will need to use :paste mode for this. So Prob[_] is our base probability monad trait, and Empirical[_] is our simplest implementation, which is just a collection of weighted particles. The method flatMap forms the naive product of empirical measures and then resamples in order to stop an explosion in the number of particles. There are two things worth noting about the resample method. The first is that the log-sum-exp trick is being used to avoid overflow and underflow when the log weights are exponentiated. The second is that although the method returns an equally weighted set of particles, the log weights are all set in order that the average raw weight of the output set matches the average raw weight of the input set. This is a little tricky to explain, but it turns out to be necessary in order to correctly propagate conditioning information back through multiple monadic binds (flatMaps). The cond method allows conditioning of a distribution using an arbitrary log-likelihood. It is included for comparison with some other implementations I will refer to later, but we won’t actually be using it, so we could save two lines of code here if necessary. The empirical method just extracts an unweighted set of values from a distribution for subsequent analysis.

It will be handy to have a function to turn a bunch of unweighted particles into a set of particles with equal weights (a sort-of inverse of the empirical method just described), so we can define that as follows.

def unweighted[T](ts: Vector[T], lw: Double = 0.0): Prob[T] =
Empirical(ts map (Particle(_, lw)))


Probabilistic programming is essentially trivial if we only care about forward sampling. But interesting PPLs allow us to condition on observed values of random variables. In the context of SMC, this is simplest when the distribution being conditioned has a tractable log-likelihood. So we can now define an extension of our probability monad for distributions with a tractable log-likelihood, and define a bunch of convenient conditioning (or “fitting”) methods using it.

trait Dist[T] extends Prob[T] {
def ll(obs: T): Double
def ll(obs: Seq[T]): Double = obs map (ll) reduce (_+_)
def fit(obs: Seq[T]): Prob[T] =
Empirical(particles map (p => Particle(p.v, p.lw + ll(obs))))
def fitQ(obs: Seq[T]): Prob[T] = Empirical(Vector(Particle(obs.head, ll(obs))))
def fit(obs: T): Prob[T] = fit(List(obs))
def fitQ(obs: T): Prob[T] = fitQ(List(obs))
}


The only unimplemented method is ll(). The fit method re-weights a particle set according to the observed log-likelihood. For convenience, it also returns a particle cloud representing the posterior-predictive distribution of an iid value from the same distribution. This is handy, but comes at the expense of introducing an additional particle cloud. So, if you aren’t interested in the posterior predictive, you can avoid this cost by using the fitQ method (for “fit quick”), which doesn’t return anything useful. We’ll see examples of this in practice, shortly. Note that the fitQ methods aren’t strictly required for our “minimal” PPL, so we can save a couple of lines by omitting them if necessary. Similarly for the variants which allow conditioning on a collection of iid observations from the same distribution.

At this point we are essentially done. But for convenience, we can define a few standard distributions to help get new users of our PPL started. Of course, since the PPL is embedded, it is trivial to add our own additional distributions later.

case class Normal(mu: Double, v: Double)(implicit N: Int) extends Dist[Double] {
lazy val particles = unweighted(bdist.Gaussian(mu, math.sqrt(v)).sample(N).toVector).particles
def ll(obs: Double) = bdist.Gaussian(mu, math.sqrt(v)).logPdf(obs) }

case class Gamma(a: Double, b: Double)(implicit N: Int) extends Dist[Double] {
lazy val particles = unweighted(bdist.Gamma(a, 1.0/b).sample(N).toVector).particles
def ll(obs: Double) = bdist.Gamma(a, 1.0/b).logPdf(obs) }

case class Poisson(mu: Double)(implicit N: Int) extends Dist[Int] {
lazy val particles = unweighted(bdist.Poisson(mu).sample(N).toVector).particles
def ll(obs: Int) = bdist.Poisson(mu).logProbabilityOf(obs) }


Note that I’ve parameterised the Normal and Gamma the way that statisticians usually do, and not the way they are usually parameterised in scientific computing libraries (such as Breeze).

That’s it! This is a complete, general-purpose, composable, monadic PPL, in 50 (actually, 48, and fewer still if you discount trailing braces) lines of code. Let’s now see how it works in practice.

## Examples

### Normal random sample

We’ll start off with just about the simplest slightly interesting example I can think of: Bayesian inference for the mean and variance of a normal distribution from a random sample.

import breeze.stats.{meanAndVariance => meanVar}
// import breeze.stats.{meanAndVariance=>meanVar}

val mod = for {
mu <- Normal(0, 100)
tau <- Gamma(1, 0.1)
_ <- Normal(mu, 1.0/tau).fitQ(List(8.0,9,7,7,8,10))
} yield (mu,tau)
// mod: Wrapped.Prob[(Double, Double)] = Empirical(Vector(Particle((8.718127116254472,0.93059589932682),-15.21683812389373), Particle((7.977706390420308,1.1575288208065433),-15.21683812389373), Particle((7.977706390420308,1.1744750937611985),-15.21683812389373), Particle((7.328100552769214,1.1181787982959164),-15.21683812389373), Particle((7.977706390420308,0.8283737237370494),-15.21683812389373), Particle((8.592847414557049,2.2934836446009026),-15.21683812389373), Particle((8.718127116254472,1.498741032928539),-15.21683812389373), Particle((8.592847414557049,0.2506065368748732),-15.21683812389373), Particle((8.543283880264225,1.127386759627675),-15.21683812389373), Particle((7.977706390420308,1.3508728798704925),-15.21683812389373), Particle((7.977706390420308,1.1134430556990933),-15.2168...

val modEmp = mod.empirical
// modEmp: Vector[(Double, Double)] = Vector((7.977706390420308,0.8748006833362748), (6.292345096890432,0.20108091703626174), (9.15330820843396,0.7654238730107492), (8.960935105658741,1.027712984079369), (7.455292602273359,0.49495749079351836), (6.911716909394562,0.7739749058662421), (6.911716909394562,0.6353785792877397), (7.977706390420308,1.1744750937611985), (7.977706390420308,1.1134430556990933), (8.718127116254472,1.166399872049532), (8.763777227034538,1.0468304705769353), (8.718127116254472,0.93059589932682), (7.328100552769214,1.6166695922250236), (8.543283880264225,0.4689300351248357), (8.543283880264225,2.0028918490755094), (7.536025958690963,0.6282318170458533), (7.328100552769214,1.6166695922250236), (7.049843463553113,0.20149378088848635), (7.536025958690963,2.3565657669819897...

meanVar(modEmp map (_._1)) // mu
// res0: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(8.311171010932343,0.4617800639333532,300)

meanVar(modEmp map (_._2)) // tau
// res1: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(0.940762723934599,0.23641881704888842,300)


Note the use of the empirical method to turn the distribution into an unweighted set of particles for Monte Carlo analysis. Anyway, the main point is that the syntactic sugar for monadic binds (flatMaps) provided by Scala’s for-expressions (similar to do-notation in Haskell) leads to readable code not so different to that in well-known general-purpose PPLs such as BUGS, JAGS, or Stan. There are some important differences, however. In particular, the embedded DSL has probabilistic programs as regular values in the host language. These may be manipulated and composed like other values. This makes this probabilistic programming language more composable than the aforementioned languages, which makes it much simpler to build large, complex probabilistic programs from simpler, well-tested, components, in a scalable way. That is, this PPL we have obtained “for free” is actually in many ways better than most well-known PPLs.

### Noisy measurements of a count

Here we’ll look at the problem of inference for a discrete count given some noisy iid continuous measurements of it.

val mod = for {
count <- Poisson(10)
tau <- Gamma(1, 0.1)
_ <- Normal(count, 1.0/tau).fitQ(List(4.2,5.1,4.6,3.3,4.7,5.3))
} yield (count, tau)
// mod: Wrapped.Prob[(Int, Double)] = Empirical(Vector(Particle((5,4.488795220669575),-11.591037521513753), Particle((5,1.7792314573063672),-11.591037521513753), Particle((5,2.5238021156137673),-11.591037521513753), Particle((4,3.280754333896923),-11.591037521513753), Particle((5,2.768438569482849),-11.591037521513753), Particle((4,1.3399975573518912),-11.591037521513753), Particle((5,1.1792835858615431),-11.591037521513753), Particle((5,1.989491156206883),-11.591037521513753), Particle((4,0.7825254987152054),-11.591037521513753), Particle((5,2.7113936834028793),-11.591037521513753), Particle((5,3.7615196800240387),-11.591037521513753), Particle((4,1.6833300961124709),-11.591037521513753), Particle((5,2.749183220798113),-11.591037521513753), Particle((5,2.1074062883430202),-11.591037521513...

val modEmp = mod.empirical
// modEmp: Vector[(Int, Double)] = Vector((4,3.243786594839479), (4,1.5090869158886693), (4,1.280656912383482), (5,2.0616356908358195), (5,3.475433097869503), (5,1.887582611202514), (5,2.8268877720514745), (5,0.9193261688050818), (4,1.7063629502805908), (5,2.116414832864841), (5,3.775508828984636), (5,2.6774941123762814), (5,2.937859946593459), (5,1.2047689975166402), (5,2.5658806161572656), (5,1.925890364268593), (4,1.0194093176888832), (5,1.883288825936725), (5,4.9503779454422965), (5,0.9045613180858916), (4,1.5795027943928661), (5,1.925890364268593), (5,2.198539449287062), (5,1.791363956348445), (5,0.9853760689818026), (4,1.6541388923071607), (5,2.599899960899971), (4,1.8904423810277957), (5,3.8983183765907836), (5,1.9242319515895554), (5,2.8268877720514745), (4,1.772120802027519), (5,2...

meanVar(modEmp map (_._1.toDouble)) // count
// res2: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(4.670000000000004,0.23521739130434777,300)

meanVar(modEmp map (_._2)) // tau
// res3: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(1.9678279101913874,0.9603971613375548,300)


I’ve included this mainly as an example of inference for a discrete-valued parameter. There are people out there who will tell you that discrete parameters are bad/evil/impossible. This isn’t true – discrete parameters are cool!

### Linear model

Because our PPL is embedded, we can take full advantage of the power of the host programming language to build our models. Let’s explore this in the context of Bayesian estimation of a linear model. We’ll start with some data.

val x = List(1.0,2,3,4,5,6)
// x: List[Double] = List(1.0, 2.0, 3.0, 4.0, 5.0, 6.0)

val y = List(3.0,2,4,5,5,6)
// y: List[Double] = List(3.0, 2.0, 4.0, 5.0, 5.0, 6.0)

val xy = x zip y
// xy: List[(Double, Double)] = List((1.0,3.0), (2.0,2.0), (3.0,4.0), (4.0,5.0), (5.0,5.0), (6.0,6.0))


Now, our (simple) linear regression model will be parameterised by an intercept, alpha, a slope, beta, and a residual variance, v. So, for convenience, let’s define an ADT representing a particular linear model.

case class Param(alpha: Double, beta: Double, v: Double)
// defined class Param


Now we can define a prior distribution over models as follows.

val prior = for {
alpha <- Normal(0,10)
beta <- Normal(0,4)
v <- Gamma(1,0.1)
} yield Param(alpha, beta, v)
// prior: Wrapped.Prob[Param] = Empirical(Vector(Particle(Param(-2.392517550699654,-3.7516090283880095,1.724680963054379),0.0), Particle(Param(7.60982717067903,-1.4318199629361292,2.9436745225038545),0.0), Particle(Param(-1.0281832158124837,-0.2799562317845073,4.05125312048092),0.0), Particle(Param(-1.0509321093485073,-2.4733837587060448,0.5856868459456287),0.0), Particle(Param(7.678898742733517,0.15616204936412104,5.064540017623097),0.0), Particle(Param(-3.392028985658713,-0.694412176170572,7.452625596437611),0.0), Particle(Param(3.0310535934425324,-2.97938526497514,2.138446100857938),0.0), Particle(Param(3.016959696424399,1.3370878561954143,6.18957854813488),0.0), Particle(Param(2.6956505371497066,1.058845844793446,5.257973123790336),0.0), Particle(Param(1.496225540527873,-1.573936445746...


Since our language doesn’t include any direct syntactic support for fitting regression models, we can define our own function for conditioning a distribution over models on a data point, which we can then apply to our prior as a fold over the available data.

def addPoint(current: Prob[Param], obs: (Double, Double)): Prob[Param] = for {
p <- current
(x, y) = obs
_ <- Normal(p.alpha + p.beta * x, p.v).fitQ(y)
} yield p
// addPoint: (current: Wrapped.Prob[Param], obs: (Double, Double))Wrapped.Prob[Param]

// mod: Vector[Param] = Vector(Param(1.4386051853067798,0.8900831186754122,4.185564696221981), Param(0.5530582357040271,1.1296886766045509,3.468527573093037), Param(0.6302560079049571,0.9396563485293532,3.7044543917875927), Param(3.68291303096638,0.4781372802435529,5.151665328789926), Param(3.016959696424399,0.4438016738989412,1.9988914122633519), Param(3.016959696424399,0.4438016738989412,1.9988914122633519), Param(0.6302560079049571,0.9396563485293532,3.7044543917875927), Param(0.6302560079049571,0.9396563485293532,3.7044543917875927), Param(3.68291303096638,0.4781372802435529,5.151665328789926), Param(3.016959696424399,0.4438016738989412,1.9988914122633519), Param(0.6302560079049571,0.9396563485293532,3.7044543917875927), Param(0.6302560079049571,0.9396563485293532,3.7044543917875927), ...

meanVar(mod map (_.alpha))
// res4: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(1.5740812481283812,1.893684802867127,300)

meanVar(mod map (_.beta))
// res5: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(0.7690238868623273,0.1054479268115053,300)

meanVar(mod map (_.v))
// res6: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(3.5240853748668695,2.793386340338213,300)


We could easily add syntactic support to our language to enable the fitting of regression-style models, as is done in Rainier, of which more later.

### Dynamic generalised linear model

The previous examples have been fairly simple, so let’s finish with something a bit less trivial. Our language is quite flexible enough to allow the analysis of a dynamic generalised linear model (DGLM). Here we’ll fit a Poisson DGLM with a log-link and a simple Brownian state evolution. More complex models are more-or-less similarly straightforward. The model is parameterised by an initial state, state0, and and evolution variance, w.

val data = List(2,1,0,2,3,4,5,4,3,2,1)
// data: List[Int] = List(2, 1, 0, 2, 3, 4, 5, 4, 3, 2, 1)

val prior = for {
w <- Gamma(1, 1)
state0 <- Normal(0.0, 2.0)
} yield (w, List(state0))
// prior: Wrapped.Prob[(Double, List[Double])] = Empirical(Vector(Particle((0.12864918092587044,List(-2.862479260552014)),0.0), Particle((1.1706344622093179,List(1.6138397233532091)),0.0), Particle((0.757288087950638,List(-0.3683499919402798)),0.0), Particle((2.755201217523856,List(-0.6527488751780317)),0.0), Particle((0.7535085397802043,List(0.5135562407906502)),0.0), Particle((1.1630726564525629,List(0.9703146201262348)),0.0), Particle((1.0080345715326213,List(-0.375686732266234)),0.0), Particle((4.603723117526974,List(-1.6977366375222938)),0.0), Particle((0.2870669117815037,List(2.2732160435099433)),0.0), Particle((2.454675218313211,List(-0.4148287542786906)),0.0), Particle((0.3612534201761152,List(-1.0099270904161748)),0.0), Particle((0.29578453393473114,List(-2.4938128878051966)),0.0)...


We can define a function to create a new hidden state, prepend it to the list of hidden states, and condition on the observed value at that time point as follows.

def addTimePoint(current: Prob[(Double, List[Double])],
obs: Int): Prob[(Double, List[Double])] = for {
tup <- current
(w, states) = tup
ns <- Normal(os, w)
_ <- Poisson(math.exp(ns)).fitQ(obs)
} yield (w, ns :: states)
// addTimePoint: (current: Wrapped.Prob[(Double, List[Double])], obs: Int)Wrapped.Prob[(Double, List[Double])]


We then run our (augmented state) particle filter as a fold over the time series.

val mod = data.foldLeft(prior)(addTimePoint(_,_)).empirical
// mod: Vector[(Double, List[Double])] = Vector((0.053073252551193446,List(0.8693030057529023, 1.2746526177834938, 1.020307245610461, 1.106341696651584, 1.070777529635013, 0.8749041525303247, 0.9866999164354662, 0.4082577920509255, 0.06903234462140699, -0.018835642776197814, -0.16841912034400547, -0.08919045681401294)), (0.0988871875952762,List(-0.24241948109998607, 0.09321618969352086, 0.9650532206325375, 1.1738734442767293, 1.2272325310228442, 0.9791695328246326, 0.5576319082578128, -0.0054280215024367084, 0.4256621012454391, 0.7486862644576158, 0.8193517409118243, 0.5928750312493785)), (0.16128799384962295,List(-0.30371187329667104, -0.3976854602292066, 0.5869357473774455, 0.9881090696832543, 1.2095181380307558, 0.7211231597865506, 0.8085486452269925, 0.2664373341459165, -0.627344024142...

meanVar(mod map (_._1)) // w
// res7: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(0.29497487517435844,0.0831412016262515,300)

meanVar(mod map (_._2.reverse.head)) // state0 (initial state)
// res8: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(0.04617218427664018,0.372844704533101,300)

meanVar(mod map (_._2.head)) // stateN (final state)
// res9: breeze.stats.meanAndVariance.MeanAndVariance = MeanAndVariance(0.4937178761565612,0.2889287607470016,300)


## Summary, conclusions, and further reading

So, we’ve seen how we can build a fully functional, general-purpose, compositional, monadic PPL from scratch in 50 lines of code, and we’ve seen how we can use it to solve real, analytically intractable Bayesian inference problems of non-trivial complexity. Of course, there are many limitations to using exactly this PPL implementation in practice. The algorithm becomes intolerably slow for deeply nested models, and uses unreasonably large amounts of RAM for large numbers of particles. It also suffers from a particle degeneracy problem if there are too many conditioning events. But it is important to understand that these are all deficiencies of the naive inference algorithm used, not the PPL itself. The PPL is flexible and compositional and can be used to build models of arbitrary size and complexity – it just needs to be underpinned by a better, more efficient, inference algorithm. Rainier is a Scala library I’ve blogged about previously which uses a very similar PPL to the one described here, but is instead underpinned by a fast, efficient, HMC algorithm. With my student Jonny Law, we have recently arXived a paper on Functional probabilistic programming for scalable Bayesian modelling, discussing some of these issues, and exploring the compositional nature of monadic PPLs (somewhat glossed over in this post).

Since the same PPL can be underpinned by different inference algorithms encapsulated as probability monads, an obvious question is whether it is possible to abstract the PPL away from the inference algorithm implementation. Of course, the answer is “yes”, and this has been explored to great effect in papers such as Practical probabilistic programming with monads and Functional programming for modular Bayesian inference. Note that they use the cond approach to conditioning, which looks a bit unwieldy, but is equivalent to fitting. As well as allowing alternative inference algorithms to be applied to the same probabilistic program, it also enables the composing of inference algorithms – for example, composing a MH algorithm with an SMC algorithm in order to get a PMMH algorithm. The ideas are implemented in an embedded DSL for Haskell, monad-bayes. If you are not used to Haskell, the syntax will probably seem a bit more intimidating than Scala’s, but the semantics are actually quite similar, with the main semantic difference being that Scala is strictly evaluated by default, whereas Haskell is lazily evaluated by default. Both languages support both lazy and strict evaluation – the difference relates simply to default behaviour, but is important nevertheless.

### Software

• min-ppl – code associated with this blog post
• Rainier – a more efficient PPL with similar syntax

## Introduction

In the previous post I gave a brief introduction to the third edition of my textbook, Stochastic modelling for systems biology. The algorithms described in the book are illustrated by implementations in R. These implementations are collected together in an R package on CRAN called smfsb. This post will provide a brief introduction to the package and its capabilities.

## Installation

The package is on CRAN – see the CRAN package page for details. So the simplest way to install it is to enter

install.packages("smfsb")


at the R command prompt. This will install the latest version that is on CRAN. Once installed, the package can be loaded with

library(smfsb)


The package is well-documented, so further information can be obtained with the usual R mechanisms, such as

vignette(package="smfsb")
vignette("smfsb")
help(package="smfsb")
?StepGillespie
example(StepCLE1D)


The version of the package on CRAN is almost certainly what you want. However, the package is developed on R-Forge – see the R-Forge project page for details. So the very latest version of the package can always be installed with

install.packages("smfsb", repos="http://R-Forge.R-project.org")


if you have a reason for wanting it.

## A brief tutorial

The vignette gives a quick introduction the the library, which I don’t need to repeat verbatim here. If you are new to the package, I recommend working through that before continuing. Here I’ll concentrate on some of the new features associated with the third edition.

### Simulating stochastic kinetic models

Much of the book is concerned with the simulation of stochastic kinetic models using exact and approximate algorithms. Although the primary focus of the text is the application to modelling of intra-cellular processes, the methods are also appropriate for population modelling of ecological and epidemic processes. For example, we can start by simulating a simple susceptible-infectious-recovered (SIR) disease epidemic model.

set.seed(2)
data(spnModels)

stepSIR = StepGillespie(SIR)
plot(simTs(SIR$M, 0, 8, 0.05, stepSIR), main="Exact simulation of the SIR model")  The focus of the text is stochastic simulation of discrete models, so that is the obvious place to start. But there is also support for continuous deterministic simulation. plot(simTs(SIR$M, 0, 8, 0.05, StepEulerSPN(SIR)),
main="Euler simulation of the SIR model")


My favourite toy population dynamics model is the Lotka-Volterra (LV) model, so I tend to use this frequently as a running example throughout the book. We can simulate this (exactly) as follows.

stepLV = StepGillespie(LV)
plot(simTs(LV$M, 0, 30, 0.2, stepLV), main="Exact simulation of the LV model")  ### Stochastic reaction-diffusion modelling The first two editions of the book were almost exclusively concerned with well-mixed systems, where spatial effects are ignorable. One of the main new features of the third edition is the inclusion of a new chapter on spatially extended systems. The focus is on models related to the reaction diffusion master equation (RDME) formulation, rather than individual particle-based simulations. For these models, space is typically divided into a regular grid of voxels, with reactions taking place as normal within each voxel, and additional reaction events included, corresponding to the diffusion of particles to adjacent voxels. So to specify such models, we just need an initial condition, a reaction model, and diffusion coefficients (one for each reacting species). So, we can carry out exact simulation of an RDME model for a 1D spatial domain as follows. N=20; T=30 x0=matrix(0, nrow=2, ncol=N) rownames(x0) = c("x1", "x2") x0[,round(N/2)] = LV$M
stepLV1D = StepGillespie1D(LV, c(0.6, 0.6))
xx = simTs1D(x0, 0, T, 0.2, stepLV1D, verb=TRUE)
image(xx[1,,], main="Prey", xlab="Space", ylab="Time")


image(xx[2,,], main="Predator", xlab="Space", ylab="Time")


Exact simulation of discrete stochastic reaction diffusion systems is very expensive (and the reference implementation provided in the package is very inefficient), so we will often use diffusion approximations based on the CLE.

stepLV1DC = StepCLE1D(LV, c(0.6, 0.6))
xx = simTs1D(x0, 0, T, 0.2, stepLV1D)
image(xx[1,,], main="Prey", xlab="Space", ylab="Time")


image(xx[2,,], main="Predator", xlab="Space", ylab="Time")


We can think of this algorithm as an explicit numerical integration of the obvious SPDE approximation to the exact model.

The package also includes support for simulation of 2D systems. Again, we can use the Spatial CLE to speed things up.

m=70; n=50; T=10
data(spnModels)
x0=array(0, c(2,m,n))
dimnames(x0)[[1]]=c("x1", "x2")
x0[,round(m/2),round(n/2)] = LV$M stepLV2D = StepCLE2D(LV, c(0.6,0.6), dt=0.05) xx = simTs2D(x0, 0, T, 0.5, stepLV2D) N = dim(xx)[4] image(xx[1,,,N],main="Prey",xlab="x",ylab="y")  image(xx[2,,,N],main="Predator",xlab="x",ylab="y")  ### Bayesian parameter inference Although much of the book is concerned with the problem of forward simulation, the final chapters are concerned with the inverse problem of estimating model parameters, such as reaction rate constants, from data. A computational Bayesian approach is adopted, with the main emphasis being placed on “likelihood free” methods, which rely on forward simulation to avoid explicit computation of sample path likelihoods. The second edition included some rudimentary code for a likelihood free particle marginal Metropolis-Hastings (PMMH) particle Markov chain Monte Carlo (pMCMC) algorithm. The third edition includes a more complete and improved implementation, in addition to approximate inference algorithms based on approximate Bayesian computation (ABC). The key function underpinning the PMMH approach is pfMLLik, which computes an estimate of marginal model log-likelihood using a (bootstrap) particle filter. There is a new implementation of this function with the third edition. There is also a generic implementation of the Metropolis-Hastings algorithm, metropolisHastings, which can be combined with pfMLLik to create a PMMH algorithm. PMMH algorithms are very slow, but a full demo of how to use these functions for parameter inference is included in the package and can be run with demo(PMCMC)  Simple rejection-based ABC methods are facilitated by the (very simple) function abcRun, which just samples from a prior and then carries out independent simulations in parallel before computing summary statistics. A simple illustration of the use of the function is given below. data(LVdata) rprior <- function() { exp(c(runif(1, -3, 3),runif(1,-8,-2),runif(1,-4,2))) } rmodel <- function(th) { simTs(c(50,100), 0, 30, 2, stepLVc, th) } sumStats <- identity ssd = sumStats(LVperfect) distance <- function(s) { diff = s - ssd sqrt(sum(diff*diff)) } rdist <- function(th) { distance(sumStats(rmodel(th))) } out = abcRun(10000, rprior, rdist) q=quantile(out$dist, c(0.01, 0.05, 0.1))
print(q)

##       1%       5%      10%
## 772.5546 845.8879 881.0573

accepted = out$param[out$dist < q[1],]
print(summary(accepted))

##        V1                V2                  V3
##  Min.   :0.06498   Min.   :0.0004467   Min.   :0.01887
##  1st Qu.:0.16159   1st Qu.:0.0012598   1st Qu.:0.04122
##  Median :0.35750   Median :0.0023488   Median :0.14664
##  Mean   :0.68565   Mean   :0.0046887   Mean   :0.36726
##  3rd Qu.:0.86708   3rd Qu.:0.0057264   3rd Qu.:0.36870
##  Max.   :4.76773   Max.   :0.0309364   Max.   :3.79220

print(summary(log(accepted)))

##        V1                V2               V3
##  Min.   :-2.7337   Min.   :-7.714   Min.   :-3.9702
##  1st Qu.:-1.8228   1st Qu.:-6.677   1st Qu.:-3.1888
##  Median :-1.0286   Median :-6.054   Median :-1.9198
##  Mean   :-0.8906   Mean   :-5.877   Mean   :-1.9649
##  3rd Qu.:-0.1430   3rd Qu.:-5.163   3rd Qu.:-0.9978
##  Max.   : 1.5619   Max.   :-3.476   Max.   : 1.3329


Naive rejection-based ABC algorithms are notoriously inefficient, so the library also includes an implementation of a more efficient, sequential version of ABC, often known as ABC-SMC, in the function abcSmc. This function requires specification of a perturbation kernel to “noise up” the particles at each algorithm sweep. Again, the implementation is parallel, using the parallel package to run the required simulations in parallel on multiple cores. A simple illustration of use is given below.

rprior <- function() { c(runif(1, -3, 3), runif(1, -8, -2), runif(1, -4, 2)) }
dprior <- function(x, ...) { dunif(x[1], -3, 3, ...) +
dunif(x[2], -8, -2, ...) + dunif(x[3], -4, 2, ...) }
rmodel <- function(th) { simTs(c(50,100), 0, 30, 2, stepLVc, exp(th)) }
rperturb <- function(th){th + rnorm(3, 0, 0.5)}
dperturb <- function(thNew, thOld, ...){sum(dnorm(thNew, thOld, 0.5, ...))}
sumStats <- identity
ssd = sumStats(LVperfect)
distance <- function(s) {
diff = s - ssd
sqrt(sum(diff*diff))
}
rdist <- function(th) { distance(sumStats(rmodel(th))) }
out = abcSmc(5000, rprior, dprior, rdist, rperturb,
dperturb, verb=TRUE, steps=6, factor=5)

## 6 5 4 3 2 1

print(summary(out))

##        V1                V2               V3
##  Min.   :-2.9961   Min.   :-7.988   Min.   :-3.999
##  1st Qu.:-1.9001   1st Qu.:-6.786   1st Qu.:-3.428
##  Median :-1.2571   Median :-6.167   Median :-2.433
##  Mean   :-1.0789   Mean   :-6.014   Mean   :-2.196
##  3rd Qu.:-0.2682   3rd Qu.:-5.261   3rd Qu.:-1.161
##  Max.   : 2.1128   Max.   :-2.925   Max.   : 1.706


We can then plot some results with

hist(out[,1],30,main="log(c1)")


hist(out[,2],30,main="log(c2)")


hist(out[,3],30,main="log(c3)")


Although the inference methods are illustrated in the book in the context of parameter inference for stochastic kinetic models, their implementation is generic, and can be used with any appropriate parameter inference problem.

## The smfsbSBML package

smfsbSBML is another R package associated with the third edition of the book. This package is not on CRAN due to its dependency on a package not on CRAN, and hence is slightly less straightforward to install. Follow the available installation instructions to install the package. Once installed, you should be able to load the package with

library(smfsbSBML)


This package provides a function for reading in SBML files and parsing them into the simulatable stochastic Petri net (SPN) objects used by the main smfsb R package. Examples of suitable SBML models are included in the main smfsb GitHub repo. An appropriate SBML model can be read and parsed with a command like:

model = sbml2spn("mySbmlModel.xml")


The resulting value, model is an SPN object which can be passed in to simulation functions such as StepGillespie for constructing stochastic simulation algorithms.

## Other software

In addition to the above R packages, I also have some Python scripts for converting between SBML and the SBML-shorthand notation I use in the book. See the SBML-shorthand page for further details.

Although R is a convenient language for teaching and learning about stochastic simulation, it isn’t ideal for serious research-level scientific computing or computational statistics. So for the third edition of the book I have also developed scala-smfsb, a library written in the Scala programming language, which re-implements all of the models and algorithms from the third edition of the book in Scala, a fast, efficient, strongly-typed, compiled, functional programming language. I’ll give an introduction to this library in a subsequent post, but in the meantime, it is already well documented, so see the scala-smfsb repo for further details, including information on installation, getting started, a tutorial, examples, API docs, etc.

## Source

This blog post started out as an RMarkdown document, the source of which can be found here.

## One-way ANOVA with fixed and random effects from a Bayesian perspective

This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it is more natural and powerful. However, some of the terminology associated with hierarchical statistical modelling probably seems a bit mysterious to those without a strong background in classical statistical modelling, and so this practical session was intended to clear up some potential confusion. I will analyse a simple one-way Analysis of Variance (ANOVA) model from a Bayesian perspective, making sure to highlight the difference between fixed and random effects in a Bayesian context where everything is random, as well as emphasising the associated identifiability issues. R code is used to illustrate the ideas.

### Example scenario

We will consider the body mass index (BMI) of new male undergraduate students at a selection of UK Universities. Let us suppose that our data consist of measurements of (log) BMI for a random sample of 1,000 males at each of 8 Universities. We are interested to know if there are any differences between the Universities. Again, we want to model the process as we would simulate it, so thinking about how we would simulate such data is instructive. We start by assuming that the log BMI is a normal random quantity, and that the variance is common across the Universities in question (this is quite a big assumption, and it is easy to relax). We assume that the mean of this normal distribution is University-specific, but that we do not have strong prior opinions regarding the way in which the Universities differ. That said, we expect that the Universities would not be very different from one another.

### Simulating data

A simple simulation of the data with some plausible parameters can be carried out as follows.

set.seed(1)
Z=matrix(rnorm(1000*8,3.1,0.1),nrow=8)
RE=rnorm(8,0,0.01)
X=t(Z+RE)
colnames(X)=paste("Uni",1:8,sep="")
Data=stack(data.frame(X))
boxplot(exp(values)~ind,data=Data,notch=TRUE)


Make sure that you understand exactly what this code is doing before proceeding. The boxplot showing the simulated data is given below.

### Frequentist analysis

We will start with a frequentist analysis of the data. The model we would like to fit is

$y_{ij} = \mu + \theta_i + \varepsilon_{ij}$

where i is an indicator for the University and j for the individual within a particular University. The “effect”, $\theta_i$ represents how the ith University differs from the overall mean. We know that this model is not actually identifiable when the model parameters are all treated as “fixed effects”, but R will handle this for us.

> mod=lm(values~ind,data=Data)
> summary(mod)

Call:
lm(formula = values ~ ind, data = Data)

Residuals:
Min       1Q   Median       3Q      Max
-0.36846 -0.06778 -0.00069  0.06910  0.38219

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  3.101068   0.003223 962.244  < 2e-16 ***
indUni2     -0.006516   0.004558  -1.430 0.152826
indUni3     -0.017168   0.004558  -3.767 0.000166 ***
indUni4      0.017916   0.004558   3.931 8.53e-05 ***
indUni5     -0.022838   0.004558  -5.011 5.53e-07 ***
indUni6     -0.001651   0.004558  -0.362 0.717143
indUni7      0.007935   0.004558   1.741 0.081707 .
indUni8      0.003373   0.004558   0.740 0.459300
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1019 on 7992 degrees of freedom
Multiple R-squared:  0.01439,	Adjusted R-squared:  0.01353
F-statistic: 16.67 on 7 and 7992 DF,  p-value: < 2.2e-16


We see that R has handled the identifiability problem using “treatment contrasts”, dropping the fixed effect for the first university, so that the intercept actually represents the mean value for the first University, and the effects for the other Univeristies represent the differences from the first University. If we would prefer to impose a sum constraint, then we can switch to sum contrasts with

options(contrasts=rep("contr.sum",2))


and then re-fit the model.

> mods=lm(values~ind,data=Data)
> summary(mods)

Call:
lm(formula = values ~ ind, data = Data)

Residuals:
Min       1Q   Median       3Q      Max
-0.36846 -0.06778 -0.00069  0.06910  0.38219

Coefficients:
Estimate Std. Error  t value Pr(>|t|)
(Intercept)  3.0986991  0.0011394 2719.558  < 2e-16 ***
ind1         0.0023687  0.0030146    0.786 0.432048
ind2        -0.0041477  0.0030146   -1.376 0.168905
ind3        -0.0147997  0.0030146   -4.909 9.32e-07 ***
ind4         0.0202851  0.0030146    6.729 1.83e-11 ***
ind5        -0.0204693  0.0030146   -6.790 1.20e-11 ***
ind6         0.0007175  0.0030146    0.238 0.811889
ind7         0.0103039  0.0030146    3.418 0.000634 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1019 on 7992 degrees of freedom
Multiple R-squared:  0.01439,	Adjusted R-squared:  0.01353
F-statistic: 16.67 on 7 and 7992 DF,  p-value: < 2.2e-16


This has 7 degrees of freedom for the effects, as before, but ensures that the 8 effects sum to precisely zero. This is arguably more interpretable in this case.

### Bayesian analysis

We will now analyse the simulated data from a Bayesian perspective, using JAGS.

#### Fixed effects

All parameters in Bayesian models are uncertain, and therefore random, so there is much confusion regarding the difference between “fixed” and “random” effects in a Bayesian context. For “fixed” effects, our prior captures the idea that we sample the effects independently from a “fixed” (typically vague) prior distribution. We could simply code this up and fit it in JAGS as follows.

require(rjags)
n=dim(X)[1]
p=dim(X)[2]
data=list(X=X,n=n,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (j in 1:p) {
theta[j]~dnorm(0,0.0001)
for (i in 1:n) {
X[i,j]~dnorm(mu+theta[j],tau)
}
}
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)
autocorr.plot(output)
pairs(as.matrix(output))
crosscorr.plot(output)


On running the code we can clearly see that this naive approach leads to high posterior correlation between the mean and the effects, due to the fundamental lack of identifiability of the model. This also leads to MCMC mixing problems, but it is important to understand that this computational issue is conceptually entirely separate from the fundamental statisticial identifiability issue. Even if we could avoid MCMC entirely, the identifiability issue would remain.

A quick fix for the identifiability issue is to use “treatment contrasts”, just as for the frequentist model. We can implement that as follows.

data=list(X=X,n=n,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (j in 1:p) {
for (i in 1:n) {
X[i,j]~dnorm(mu+theta[j],tau)
}
}
theta[1]<-0
for (j in 2:p) {
theta[j]~dnorm(0,0.0001)
}
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)
autocorr.plot(output)
pairs(as.matrix(output))
crosscorr.plot(output)


Running this we see that the model now works perfectly well, mixes nicely, and gives sensible inferences for the treatment effects.

Another source of confusion for models of this type is data formating and indexing in JAGS models. For our balanced data there was not problem passing in data to JAGS as a matrix and specifying the model using nested loops. However, for unbalanced designs this is not necessarily so convenient, and so then it can be helpful to specify the model based on two-column data, as we would use for fitting using lm(). This is illustrated with the following model specification, which is exactly equivalent to the previous model, and should give identical (up to Monte Carlo error) results.

N=n*p
data=list(y=Data$values,g=Data$ind,N=N,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (i in 1:N) {
y[i]~dnorm(mu+theta[g[i]],tau)
}
theta[1]<-0
for (j in 2:p) {
theta[j]~dnorm(0,0.0001)
}
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)


As suggested above, this indexing scheme is much more convenient for unbalanced data, and hence widely used. However, since our data is balanced here, we will revert to the matrix approach for the remainder of the post.

One final thing to consider before moving on to random effects is the sum-contrast model. We can implement this in various ways, but I’ve tried to encode it for maximum clarity below, imposing the sum-to-zero constraint via the final effect.

data=list(X=X,n=n,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (j in 1:p) {
for (i in 1:n) {
X[i,j]~dnorm(mu+theta[j],tau)
}
}
for (j in 1:(p-1)) {
theta[j]~dnorm(0,0.0001)
}
theta[p] <- -sum(theta[1:(p-1)])
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)


Again, this works perfectly well and gives similar results to the frequentist analysis.

#### Random effects

The key difference between fixed and random effects in a Bayesian framework is that random effects are not independent, being drawn from a distribution with parameters which are not fixed. Essentially, there is another level of hierarchy involved in the specification of the random effects. This is best illustrated by example. A random effects model for this problem is given below.

data=list(X=X,n=n,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (j in 1:p) {
theta[j]~dnorm(0,taut)
for (i in 1:n) {
X[i,j]~dnorm(mu+theta[j],tau)
}
}
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
taut~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","taut","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)


The only difference between this and our first naive attempt at a Bayesian fixed effects model is that we have put a gamma prior on the precision of the effect. Note that this model now runs and fits perfectly well, with reasonable mixing, and gives sensible parameter inferences. Although the effects here are not constrained to sum-to-zero, like in the case of sum contrasts for a fixed effects model, the prior encourages shrinkage towards zero, and so the random effect distribution can be thought of as a kind of soft version of a hard sum-to-zero constraint. From a predictive perspective, this model is much more powerful. In particular, using a random effects model, we can make strong predictions for unobserved groups (eg. a ninth University), with sensible prediction intervals based on our inferred understanding of how similar different universities are. Using a fixed effects model this isn’t really possible. Even for a Bayesian version of a fixed effects model using proper (but vague) priors, prediction intervals for unobserved groups are not really sensible.

Since we have used simulated data here, we can compare the estimated random effects with the true effects generated during the simulation.

> apply(as.matrix(output),2,mean)
mu           tau          taut      theta[1]      theta[2]
3.098813e+00  9.627110e+01  7.015976e+03  2.086581e-03 -3.935511e-03
theta[3]      theta[4]      theta[5]      theta[6]      theta[7]
-1.389099e-02  1.881528e-02 -1.921854e-02  5.640306e-04  9.529532e-03
theta[8]
5.227518e-03
> RE
[1]  0.002637034 -0.008294518 -0.014616348  0.016839902 -0.015443243
[6] -0.001908871  0.010162117  0.005471262


We see that the Bayesian random effects model has done an excellent job of estimation. If we wished, we could relax the assumption of common variance across the groups by making tau a vector indexed by j, though there is not much point in persuing this here, since we know that the groups do all have the same variance.

#### Strong subjective priors

The above is the usual story regarding fixed and random effects in Bayesian inference. I hope this is reasonably clear, so really I should quit while I’m ahead… However, the issues are really a bit more subtle than I’ve suggested. The inferred precision of the random effects was around 7,000, so now lets re-run the original, naive, “fixed effects” model with a strong subjective Bayesian prior on the distribution of the effects.

data=list(X=X,n=n,p=p)
init=list(mu=2,tau=1)
modelstring="
model {
for (j in 1:p) {
theta[j]~dnorm(0,7000)
for (i in 1:n) {
X[i,j]~dnorm(mu+theta[j],tau)
}
}
mu~dnorm(0,0.0001)
tau~dgamma(1,0.0001)
}
"
model=jags.model(textConnection(modelstring),data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,variable.names=c("mu","tau","theta"),n.iter=100000,thin=10)
print(summary(output))
plot(output)


This model also runs perfectly well and gives sensible inferences, despite the fact that the effects are iid from a fixed distribution and there is no hard constraint on the effects. Similarly, we can make sensible predictions, together with appropriate prediction intervals, for an unobserved group. So it isn’t so much the fact that the effects are coupled via an extra level of hierarchy that makes things work. It’s really the fact that the effects are sensibly distributed and not just sampled directly from a vague prior. So for “real” subjective Bayesians the line between fixed and random effects is actually very blurred indeed…

## Introduction

In the previous post I showed that it is possible to couple parallel tempered MCMC chains in order to improve mixing. Such methods can be used when the target of interest is a Bayesian posterior distribution that is difficult to sample. There are (at least) a couple of obvious ways that one can temper a Bayesian posterior distribution. Perhaps the most obvious way is a simple flattening, so that if

$\pi(\theta|y) \propto \pi(\theta)\pi(y|\theta)$

is the posterior distribution, then for $t\in [0,1]$ we define

$\pi_t(\theta|y) \propto \pi(\theta|y)^t \propto [ \pi(\theta)\pi(y|\theta) ]^t.$

This corresponds with the tempering that is often used in statistical physics applications. We recover the posterior of interest for $t=1$ and tend to a flat distribution as $t\longrightarrow 0$. However, for Bayesian posterior distributions, there is a different way of tempering that is often more natural and useful, and that is to temper using the power posterior, defined by

$\pi_t(\theta|y) \propto \pi(\theta)\pi(y|\theta)^t.$

Here we again recover the posterior for $t=1$, but get the prior for $t=0$. Thus, the family of distributions forms a natural bridge or path from the prior to the posterior distributions. The power posterior is a special case of the more general concept of a geometric path from distribution $f(\theta)$ (at $t=0$) to $g(\theta)$ (at $t=1$) defined by

$h_t(\theta) \propto f(\theta)^{1-t}g(\theta)^t,$

where, in our case, $f(\cdot)$ is the prior and $g(\cdot)$ is the posterior.

So, given a posterior distribution that is difficult to sample, choose a temperature schedule

$0=t_0

and run a parallel tempering scheme as outlined in the previous post. The idea is that for small values of $t$ mixing will be good, as prior-like distributions are usually well-behaved, and the mixing of these "high temperature" chains can help to improve the mixing of the "low temperature" chains that are more like the posterior (note that $t$ is really an inverse temperature parameter the way I’ve defined it here…).

## Marginal likelihood and normalising constants

The marginal likelihood of a Bayesian model is

$\pi(y) = \int_\Theta \pi(\theta)\pi(y|\theta)d\theta.$

This quantity is of interest for many reasons, including calculation of the Bayes factor between two competing models. Note that this quantity has several different names in different fields. In particular, it is often known as the evidence, due to its role in Bayes factors. It is also worth noting that it is the normalising constant of the Bayesian posterior distribution. Although it is very easy to describe and define, it is notoriously difficult to compute reliably for complex models.

The normalising constant is conceptually very easy to estimate. From the above integral representation, it is clear that

$\pi(y) = E_\pi [ \pi(y|\theta) ]$

where the expectation is taken with respect to the prior. So, given samples from the prior, $\theta_1,\theta_2,\ldots,\theta_n$, we can construct the Monte Carlo estimate

$\displaystyle \widehat{\pi}(y) = \frac{1}{n}\sum_{i=1}^n \pi(y|\theta_i)$

and this will be a consistent estimator of the true evidence under fairly mild regularity conditions. Unfortunately, in practice it is likely to be a very poor estimator if the posterior and prior are not very similar. Now, we could also use Bayes theorem to re-write the integral as an expectation with respect to the posterior, so we could then use samples from the posterior to estimate the evidence. This leads to the harmonic mean estimator of the evidence, which has been described as the worst Monte Carlo method ever! Now it turns out that there are many different ways one can construct estimators of the evidence using samples from the prior and the posterior, some of which are considerably better than the two I’ve outlined. This is the subject of the bridge sampling paper of Meng and Wong. However, the reality is that no method will work well if the prior and posterior are very different.

If we have tempered chains, then we have a sequence of chains targeting distributions which, by construction, are not too different, and so we can use the output from tempered chains in order to construct estimators of the evidence that are more numerically stable. If we call the evidence of the $i$th chain $z_i$, so that $z_0=1$ and $z_N=\pi(y)$, then we can write the evidence in telescoping fashion as

$\displaystyle \pi(y)=z_N = \frac{z_N}{z_0} = \frac{z_1}{z_0}\times \frac{z_2}{z_1}\times \cdots \times \frac{z_N}{z_{N-1}}.$

Now the $i$th term in this product is $z_{i+1}/z_{i}$, which can be estimated using the output from the $i$th and/or $(i+1)$th chain(s). Again, this can be done in a variety of ways, using your favourite bridge sampling estimator, but the point is that the estimator should be reasonably good due to the fact that the $i$th and $(i+1)$th targets are very similar. For the power posterior, the simplest method is to write

$\displaystyle \frac{z_{i+1}}{z_i} = \frac{\displaystyle \int \pi(\theta)\pi(y|\theta)^{t_{i+1}}d\theta}{z_i} = \int \pi(y|\theta)^{t_{i+1}-t_i}\times \frac{\pi(y|\theta)^{t_i}\pi(\theta)}{z_i}d\theta$

$\displaystyle \mbox{}\qquad = E_i\left[\pi(y|\theta)^{t_{i+1}-t_i}\right],$

where the expectation is with respect to the $i$th target, and hence can be estimated in the usual way using samples from the $i$th chain.

For numerical stability, in practice we compute the log of the evidence as

$\displaystyle \log\pi(y) = \sum_{i=0}^{N-1} \log\frac{z_{i+1}}{z_i} = \sum_{i=0}^{N-1} \log E_i\left[\pi(y|\theta)^{t_{i+1}-t_i}\right]$

$\displaystyle = \sum_{i=0}^{N-1} \log E_i\left[\exp\{(t_{i+1}-t_i)\log\pi(y|\theta)\}\right].\qquad(\dagger)$

The above expression is exact, and is the obvious formula to use for computation. However, it is clear that if $t_i$ and $t_{i+1}$ are sufficiently close, it will be approximately OK to switch the expectation and exponential, giving

$\displaystyle \log\pi(y) \approx \sum_{i=0}^{N-1}(t_{i+1}-t_i)E_i\left[\log\pi(y|\theta)\right].$

In the continuous limit, this gives rise to the well-known path sampling identity,

$\displaystyle \log\pi(y) = \int_0^1 E_t\left[\log\pi(y|\theta)\right]dt.$

So, an alternative approach to computing the evidence is to use the samples to approximately numerically integrate the above integral, say, using the trapezium rule. However, it isn’t completely clear (to me) that this is better than using $(\dagger)$ directly, since there there is no numerical integration error to worry about.

## Numerical illustration

We can illustrate these ideas using the simple double potential well example from the previous post. Now that example doesn’t really correspond to a Bayesian posterior, and is tempered directly, rather than as a power posterior, but essentially the same ideas follow for general parallel tempered distributions. In general, we can use the sample to estimate the ratio of the last and first normalising constants, $z_N/z_0$. Here it isn’t obvious why we’d want to know that, but we’ll compute it anyway to illustrate the method. As before, we expand as a telescopic product, where the $i$th term is now

$\displaystyle \frac{z_{i+1}}{z_i} = E_i\left[\exp\{-(\gamma_{i+1}-\gamma_i)(x^2-1)^2\}\right].$

A Monte Carlo estimate of each of these terms is formed using the samples from the $i$th chain, and the logs of these are then summed to give $\log(z_N/z_0)$. A complete R script to run the Metropolis coupled sampler and compute the evidence is given below.

U=function(gam,x)
{
gam*(x*x-1)*(x*x-1)
}

temps=2^(0:3)
iters=1e5

chains=function(pot=U, tune=0.1, init=1)
{
x=rep(init,length(temps))
xmat=matrix(0,iters,length(temps))
for (i in 1:iters) {
can=x+rnorm(length(temps),0,tune)
logA=unlist(Map(pot,temps,x))-unlist(Map(pot,temps,can))
accept=(log(runif(length(temps)))<logA)
x[accept]=can[accept]
swap=sample(1:length(temps),2)
logA=pot(temps[swap[1]],x[swap[1]])+pot(temps[swap[2]],x[swap[2]])-
pot(temps[swap[1]],x[swap[2]])-pot(temps[swap[2]],x[swap[1]])
if (log(runif(1))<logA)
x[swap]=rev(x[swap])
xmat[i,]=x
}
colnames(xmat)=paste("gamma=",temps,sep="")
xmat
}

mat=chains()
mat=mat[,1:(length(temps)-1)]
diffs=diff(temps)
mat=(mat*mat-1)^2
mat=-t(diffs*t(mat))
mat=exp(mat)
logEvidence=sum(log(colMeans(mat)))
message(paste("The log of the ratio of the last and first normalising constants is",logEvidence))


It turns out that these double well potential densities are tractable, and so the normalising constants can be computed exactly. So, with a little help from Wolfram Alpha, I compute log of the ratio of the last and first normalising constants to be approximately -1.12. Hopefully the above script will output something a bit like that…

## Summary stats for ABC

#### Introduction

In the previous post I gave a very brief introduction to ABC, including a simple example for inferring the parameters of a Markov process given some time series observations. Towards the end of the post I observed that there were (at least!) two potential problems with scaling up the simple approach described, one relating to the dimension of the data and the other relating to the dimension of the parameter space. Before moving on to the (to me, more interesting) problem of the dimension of the parameter space, I will briefly discuss the data dimension problem in this post, and provide a couple of references for further reading.

#### Summary stats

Recall that the simple rejection sampling approach to ABC involves first sampling a candidate parameter $\theta^\star$ from the prior and then sampling a corresponding data set $x^\star$ from the model. This simulated data set is compared with the true data $x$ using some (pseudo-)norm, $\Vert\cdot\Vert$, and accepting $\theta^\star$ if the simulated data set is sufficiently close to the true data, $\Vert x^\star - x\Vert <\epsilon$. It should be clear that if we are using a proper norm then as $\epsilon$ tends to zero the distribution of the accepted values tends to the desired posterior distribution of the parameters given the data.

However, smaller choices of $\epsilon$ will lead to higher rejection rates. This will be a particular problem in the context of high-dimensional $x$, where it is often unrealistic to expect a close match between all components of $x$ and the simulated data $x^\star$, even for a good choice of $\theta^\star$. In this case, it makes more sense to look for good agreement between particular aspects of $x$, such as the mean, or variance, or auto-correlation, depending on the exact problem and context. If we can find a finite set of sufficient statistics, $s(x)$ for $\theta$, then it should be clear that replacing the acceptance criterion with $\Vert s(x^\star) - s(x)\Vert <\epsilon$ will also lead to a scheme tending to the true posterior as $\epsilon$ tends to zero (assuming a proper norm on the space of sufficient statistics), and will typically be better than the naive method, since the sufficient statistics will be of lower dimension and less “noisy” that the raw data, leading to higher acceptance rates with no loss of information.

Unfortunately for most problems of practical interest it is not possible to find low-dimensional sufficient statistics, and so people in practice use domain knowledge and heuristics to come up with a set of summary statistics, $s(x)$ which they hope will closely approximate sufficient statistics. There is still a question as to how these statistics should be weighted or transformed to give a particular norm. This can be done using theory or heuristics, and some relevant references for this problem are given at the end of the post.

#### Implementation in R

Let’s now look at the problem from the previous post. Here, instead of directly computing the Euclidean distance between the real and simulated data, we will look at the Euclidean distance between some (normalised) summary statistics. First we will load some packages and set some parameters.

require(smfsb)
require(parallel)
options(mc.cores=4)
data(LVdata)

N=1e7
bs=1e5
batches=N/bs
message(paste("N =",N," | bs =",bs," | batches =",batches))


Next we will define some summary stats for a univariate time series – the mean, the (log) variance, and the first two auto-correlations.

ssinit <- function(vec)
{
ac23=as.vector(acf(vec,lag.max=2,plot=FALSE)$acf)[2:3] c(mean(vec),log(var(vec)+1),ac23) }  Once we have this, we can define some stats for a bivariate time series by combining the stats for the two component series, along with the cross-correlation between them. ssi <- function(ts) { c(ssinit(ts[,1]),ssinit(ts[,2]),cor(ts[,1],ts[,2])) }  This gives a set of summary stats, but these individual statistics are potentially on very different scales. They can be transformed and re-weighted in a variety of ways, usually on the basis of a pilot run which gives some information about the distribution of the summary stats. Here we will do the simplest possible thing, which is to normalise the variance of the stats on the basis of a pilot run. This is not at all optimal – see the references at the end of the post for a description of better methods. message("Batch 0: Pilot run batch") prior=cbind(th1=exp(runif(bs,-6,2)),th2=exp(runif(bs,-6,2)),th3=exp(runif(bs,-6,2))) rows=lapply(1:bs,function(i){prior[i,]}) samples=mclapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)}) sumstats=mclapply(samples,ssi) sds=apply(sapply(sumstats,c),1,sd) print(sds) # now define a standardised distance ss<-function(ts) { ssi(ts)/sds } ss0=ss(LVperfect) distance <- function(ts) { diff=ss(ts)-ss0 sum(diff*diff) }  Now we have a normalised distance function defined, we can proceed exactly as before to obtain an ABC posterior via rejection sampling. post=NULL for (i in 1:batches) { message(paste("batch",i,"of",batches)) prior=cbind(th1=exp(runif(bs,-6,2)),th2=exp(runif(bs,-6,2)),th3=exp(runif(bs,-6,2))) rows=lapply(1:bs,function(i){prior[i,]}) samples=mclapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)}) dist=mclapply(samples,distance) dist=sapply(dist,c) cutoff=quantile(dist,1000/N,na.rm=TRUE) post=rbind(post,prior[dist<cutoff,]) } message(paste("Finished. Kept",dim(post)[1],"simulations"))  Having obtained the posterior, we can use the following code to plot the results. th=c(th1 = 1, th2 = 0.005, th3 = 0.6) op=par(mfrow=c(2,3)) for (i in 1:3) { hist(post[,i],30,col=5,main=paste("Posterior for theta[",i,"]",sep="")) abline(v=th[i],lwd=2,col=2) } for (i in 1:3) { hist(log(post[,i]),30,col=5,main=paste("Posterior for log(theta[",i,"])",sep="")) abline(v=log(th[i]),lwd=2,col=2) } par(op)  This gives the plot shown below. From this we can see that the ABC posterior obtained here is very similar to that obtained in the previous post using the full data. Here the dimension reduction is not that great – reducing from 32 data points to 9 summary statistics – and so the improvement in performance is not that noticable. But in higher dimensional problems reducing the dimension of the data is practically essential. #### Summary and References As before, I recommend the wikipedia article on approximate Bayesian computation for further information and a comprehensive set of references for further reading. Here I just want to highlight two references particularly relevant to the issue of summary statistics. It is quite difficult to give much practical advice on how to construct good summary statistics, but how to transform a set of summary stats in a “good” way is a problem that is reasonably well understood. In this post I did something rather naive (normalising the variance), but the following two papers describe much better approaches. I still haven’t addressed the issue of a high-dimensional parameter space – that will be the topic of a subsequent post. #### The complete R script require(smfsb) require(parallel) options(mc.cores=4) data(LVdata) N=1e6 bs=1e5 batches=N/bs message(paste("N =",N," | bs =",bs," | batches =",batches)) ssinit <- function(vec) { ac23=as.vector(acf(vec,lag.max=2,plot=FALSE)$acf)[2:3]
c(mean(vec),log(var(vec)+1),ac23)
}

ssi <- function(ts)
{
c(ssinit(ts[,1]),ssinit(ts[,2]),cor(ts[,1],ts[,2]))
}

message("Batch 0: Pilot run batch")
prior=cbind(th1=exp(runif(bs,-6,2)),th2=exp(runif(bs,-6,2)),th3=exp(runif(bs,-6,2)))
rows=lapply(1:bs,function(i){prior[i,]})
samples=mclapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)})
sumstats=mclapply(samples,ssi)
sds=apply(sapply(sumstats,c),1,sd)
print(sds)

# now define a standardised distance
ss<-function(ts)
{
ssi(ts)/sds
}

ss0=ss(LVperfect)

distance <- function(ts)
{
diff=ss(ts)-ss0
sum(diff*diff)
}

post=NULL
for (i in 1:batches) {
message(paste("batch",i,"of",batches))
prior=cbind(th1=exp(runif(bs,-6,2)),th2=exp(runif(bs,-6,2)),th3=exp(runif(bs,-6,2)))
rows=lapply(1:bs,function(i){prior[i,]})
samples=mclapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)})
dist=mclapply(samples,distance)
dist=sapply(dist,c)
cutoff=quantile(dist,1000/N,na.rm=TRUE)
post=rbind(post,prior[dist<cutoff,])
}
message(paste("Finished. Kept",dim(post)[1],"simulations"))

# plot the results
th=c(th1 = 1, th2 = 0.005, th3 = 0.6)
op=par(mfrow=c(2,3))
for (i in 1:3) {
hist(post[,i],30,col=5,main=paste("Posterior for theta[",i,"]",sep=""))
abline(v=th[i],lwd=2,col=2)
}
for (i in 1:3) {
hist(log(post[,i]),30,col=5,main=paste("Posterior for log(theta[",i,"])",sep=""))
abline(v=log(th[i]),lwd=2,col=2)
}
par(op)


## Introduction to Approximate Bayesian Computation (ABC)

Many of the posts in this blog have been concerned with using MCMC based methods for Bayesian inference. These methods are typically “exact” in the sense that they have the exact posterior distribution of interest as their target equilibrium distribution, but are obviously “approximate”, in that for any finite amount of computing time, we can only generate a finite sample of correlated realisations from a Markov chain that we hope is close to equilibrium.

Approximate Bayesian Computation (ABC) methods go a step further, and generate samples from a distribution which is not the true posterior distribution of interest, but a distribution which is hoped to be close to the real posterior distribution of interest. There are many variants on ABC, and I won’t get around to explaining all of them in this blog. The wikipedia page on ABC is a good starting point for further reading. In this post I’ll explain the most basic rejection sampling version of ABC, and in a subsequent post, I’ll explain a sequential refinement, often referred to as ABC-SMC. As usual, I’ll use R code to illustrate the ideas.

#### Basic idea

There is a close connection between “likelihood free” MCMC methods and those of approximate Bayesian computation (ABC). To keep things simple, consider the case of a perfectly observed system, so that there is no latent variable layer. Then there are model parameters $\theta$ described by a prior $\pi(\theta)$, and a forwards-simulation model for the data $x$, defined by $\pi(x|\theta)$. It is clear that a simple algorithm for simulating from the desired posterior $\pi(\theta|x)$ can be obtained as follows. First simulate from the joint distribution $\pi(\theta,x)$ by simulating $\theta^\star\sim\pi(\theta)$ and then $x^\star\sim \pi(x|\theta^\star)$. This gives a sample $(\theta^\star,x^\star)$ from the joint distribution. A simple rejection algorithm which rejects the proposed pair unless $x^\star$ matches the true data $x$ clearly gives a sample from the required posterior distribution.

#### Exact rejection sampling

• 1. Sample $\theta^\star \sim \pi(\theta^\star)$
• 2. Sample $x^\star\sim \pi(x^\star|\theta^\star)$
• 3. If $x^\star=x$, keep $\theta^\star$ as a sample from $\pi(\theta|x)$, otherwise reject.

This algorithm is exact, and for discrete $x$ will have a non-zero acceptance rate. However, in most interesting problems the rejection rate will be intolerably high. In particular, the acceptance rate will typically be zero for continuous valued $x$.

#### ABC rejection sampling

The ABC “approximation” is to accept values provided that $x^\star$ is “sufficiently close” to $x$. In the first instance, we can formulate this as follows.

• 1. Sample $\theta^\star \sim \pi(\theta^\star)$
• 2. Sample $x^\star\sim \pi(x^\star|\theta^\star)$
• 3. If $\Vert x^\star-x\Vert< \epsilon$, keep $\theta^\star$ as a sample from $\pi(\theta|x)$, otherwise reject.

Euclidean distance is usually chosen as the norm, though any norm can be used. This procedure is “honest”, in the sense that it produces exact realisations from

$\theta^\star\big|\Vert x^\star-x\Vert < \epsilon.$

For suitable small choice of $\epsilon$, this will closely approximate the true posterior. However, smaller choices of $\epsilon$ will lead to higher rejection rates. This will be a particular problem in the context of high-dimensional $x$, where it is often unrealistic to expect a close match between all components of $x$ and the simulated data $x^\star$, even for a good choice of $\theta^\star$. In this case, it makes more sense to look for good agreement between particular aspects of $x$, such as the mean, or variance, or auto-correlation, depending on the exact problem and context.

In the simplest case, this is done by forming a (vector of) summary statistic(s), $s(x^\star)$ (ideally a sufficient statistic), and accepting provided that $\Vert s(x^\star)-s(x)\Vert<\epsilon$ for some suitable choice of metric and $\epsilon$. We will return to this issue in a subsequent post.

#### Inference for an intractable Markov process

I’ll illustrate ABC in the context of parameter inference for a Markov process with an intractable transition kernel: the discrete stochastic Lotka-Volterra model. A function for simulating exact realisations from the intractable kernel is included in the smfsb CRAN package discussed in a previous post. Using pMCMC to solve the parameter inference problem is discussed in another post. It may be helpful to skim through those posts quickly to become familiar with this problem before proceeding.

So, for a given proposed set of parameters, realisations from the process can be sampled using the functions simTs and stepLV (from the smfsb package). We will use the sample data set LVperfect (from the LVdata dataset) as our “true”, or “target” data, and try to find parameters for the process which are consistent with this data. A fairly minimal R script for this problem is given below.

require(smfsb)
data(LVdata)

N=1e5
message(paste("N =",N))
prior=cbind(th1=exp(runif(N,-6,2)),th2=exp(runif(N,-6,2)),th3=exp(runif(N,-6,2)))
rows=lapply(1:N,function(i){prior[i,]})
message("starting simulation")
samples=lapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)})
message("finished simulation")

distance<-function(ts)
{
diff=ts-LVperfect
sum(diff*diff)
}

message("computing distances")
dist=lapply(samples,distance)
message("distances computed")

dist=sapply(dist,c)
cutoff=quantile(dist,1000/N)
post=prior[dist<cutoff,]

op=par(mfrow=c(2,3))
apply(post,2,hist,30)
apply(log(post),2,hist,30)
par(op)


This script should take 5-10 minutes to run on a decent laptop, and will result in histograms of the posterior marginals for the components of $\theta$ and $\log(\theta)$. Note that I have deliberately adopted a functional programming style, making use of the lapply function for the most computationally intensive steps. The reason for this will soon become apparent. Note that rather than pre-specifying a cutoff $\epsilon$, I’ve instead picked a quantile of the distance distribution. This is common practice in scenarios where the distance is difficult to have good intuition about. In fact here I’ve gone a step further and chosen a quantile to give a final sample of size 1000. Obviously then in this case I could have just selected out the top 1000 directly, but I wanted to illustrate the quantile based approach.

One problem with the above script is that all proposed samples are stored in memory at once. This is problematic for problems involving large numbers of samples. However, it is convenient to do simulations in large batches, both for computation of quantiles, and also for efficient parallelisation. The script below illustrates how to implement a batch parallelisation strategy for this problem. Samples are generated in batches of size 10^4, and only the best fitting samples are stored before the next batch is processed. This strategy can be used to get a good sized sample based on a more stringent acceptance criterion at the cost of addition simulation time. Note that the parallelisation code will only work with recent versions of R, and works by replacing calls to lapply with the parallel version, mclapply. You should notice an appreciable speed-up on a multicore machine.

require(smfsb)
require(parallel)
options(mc.cores=4)
data(LVdata)

N=1e5
bs=1e4
batches=N/bs
message(paste("N =",N," | bs =",bs," | batches =",batches))

distance<-function(ts)
{
diff=ts[,1]-LVprey
sum(diff*diff)
}

post=NULL
for (i in 1:batches) {
message(paste("batch",i,"of",batches))
prior=cbind(th1=exp(runif(bs,-6,2)),th2=exp(runif(bs,-6,2)),th3=exp(runif(bs,-6,2)))
rows=lapply(1:bs,function(i){prior[i,]})
samples=mclapply(rows,function(th){simTs(c(50,100),0,30,2,stepLVc,th)})
dist=mclapply(samples,distance)
dist=sapply(dist,c)
cutoff=quantile(dist,1000/N)
post=rbind(post,prior[dist<cutoff,])
}
message(paste("Finished. Kept",dim(post)[1],"simulations"))

op=par(mfrow=c(2,3))
apply(post,2,hist,30)
apply(log(post),2,hist,30)
par(op)


Note that there is an additional approximation here, since the top 100 samples from each of 10 batches of simulations won’t correspond exactly to the top 1000 samples overall, but given all of the other approximations going on in ABC, this one is likely to be the least of your worries.

Now, if you compare the approximate posteriors obtained here with the “true” posteriors obtained in an earlier post using pMCMC, you will see that these posteriors are really quite poor. However, this isn’t a very fair comparison, since we’ve only done 10^5 simulations. Jacking N up to 10^7 gives the ABC posterior below.

This is a bit better, but really not great. There are two basic problems with the simplistic ABC strategy adopted here, one related to the dimensionality of the data and the other the dimensionality of the parameter space. The most basic problem that we have here is the dimensionality of the data. We have 16 (bivariate) observations, so we want our stochastic simulation to shoot at a point in a 16- or 32-dimensional space. That’s a tough call. The standard way to address this problem is to reduce the dimension of the data by introducing a few carefully chosen summary statistics and then just attempting to hit those. I’ll illustrate this in a subsequent post. The other problem is that often the prior and posterior over the parameters are quite different, and this problem too is exacerbated as the dimension of the parameter space increases. The standard way to deal with this is to sequentially adapt from the prior through a sequence of ABC posteriors. I’ll examine this in a future post as well, once I’ve also posted an introduction to the use of sequential Monte Carlo (SMC) samplers for static problems.

For further reading, I suggest browsing the reference list of the Wikipedia page for ABC. Also look through the list of software on that page. In particular, note that there is a CRAN package, abc, providing R support for ABC. There is a vignette for this package which should be sufficient to get started.

## Getting started with Bayesian variable selection using JAGS and rjags

#### Bayesian variable selection

In a previous post I gave a quick introduction to using the rjags R package to access the JAGS Bayesian inference from within R. In this post I want to give a quick guide to using rjags for Bayesian variable selection. I intend to use this post as a starting point for future posts on Bayesian model and variable selection using more sophisticated approaches.

I will use the simple example of multiple linear regression to illustrate the ideas, but it should be noted that I’m just using that as an example. It turns out that in the context of linear regression there are lots of algebraic and computational tricks which can be used to simplify the variable selection problem. The approach I give here is therefore rather inefficient for linear regression, but generalises to more complex (non-linear) problems where analytical and computational short-cuts can’t be used so easily.

Consider a linear regression problem with n observations and p covariates, which we can write in matrix form as

$y = \alpha \boldmath{1} + X\beta + \varepsilon,$

where $X$ is an $n\times p$ matrix. The idea of variable selection is that probably not all of the p covariates are useful for predicting y, and therefore it would be useful to identify the variables which are, and just use those. Clearly each combination of variables corresponds to a different model, and so the variable selection amounts to choosing among the $2^p$ possible models. For large values of p it won’t be practical to consider each possible model separately, and so the idea of Bayesian variable selection is to consider a model containing all of the possible model combinations as sub-models, and the variable selection problem as just another aspect of the model which must be estimated from data. I’m simplifying and glossing over lots of details here, but there is a very nice review paper by O’Hara and Sillanpaa (2009) which the reader is referred to for further details.

The simplest and most natural way to tackle the variable selection problem from a Bayesian perspective is to introduce an indicator random variable $I_i$ for each covariate, and introduce these into the model in order to “zero out” inactive covariates. That is we write the ith regression coefficient $\beta_i$ as $\beta_i=I_i\beta^\star_i$, so that $\beta^\star_i$ is the regression coefficient when $I_i=1$, and “doesn’t matter” when $I_i=0$. There are various ways to choose the prior over $I_i$ and $\beta^\star_i$, but the simplest and most natural choice is to make them independent. This approach was used in Kuo and Mallick (1998), and hence is referred to as the Kuo and Mallick approach in O’Hara and Sillanpaa.

#### Simulating some data

In order to see how things work, let’s first simulate some data from a regression model with geometrically decaying regression coefficients.

n=500
p=20
X=matrix(rnorm(n*p),ncol=p)
beta=2^(0:(1-p))
print(beta)
alpha=3
tau=2
eps=rnorm(n,0,1/sqrt(tau))
y=alpha+as.vector(X%*%beta + eps)


Let’s also fit the model by least squares.

mod=lm(y~X)
print(summary(mod))


This should give output something like the following.

Call:
lm(formula = y ~ X)

Residuals:
Min       1Q   Median       3Q      Max
-1.62390 -0.48917 -0.02355  0.45683  2.35448

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  3.0565406  0.0332104  92.036  < 2e-16 ***
X1           0.9676415  0.0322847  29.972  < 2e-16 ***
X2           0.4840052  0.0333444  14.515  < 2e-16 ***
X3           0.2680482  0.0320577   8.361  6.8e-16 ***
X4           0.1127954  0.0314472   3.587 0.000369 ***
X5           0.0781860  0.0334818   2.335 0.019946 *
X6           0.0136591  0.0335817   0.407 0.684379
X7           0.0035329  0.0321935   0.110 0.912662
X8           0.0445844  0.0329189   1.354 0.176257
X9           0.0269504  0.0318558   0.846 0.397968
X10          0.0114942  0.0326022   0.353 0.724575
X11         -0.0045308  0.0330039  -0.137 0.890868
X12          0.0111247  0.0342482   0.325 0.745455
X13         -0.0584796  0.0317723  -1.841 0.066301 .
X14         -0.0005005  0.0343499  -0.015 0.988381
X15         -0.0410424  0.0334723  -1.226 0.220742
X16          0.0084832  0.0329650   0.257 0.797026
X17          0.0346331  0.0327433   1.058 0.290718
X18          0.0013258  0.0328920   0.040 0.967865
X19         -0.0086980  0.0354804  -0.245 0.806446
X20          0.0093156  0.0342376   0.272 0.785671
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7251 on 479 degrees of freedom
Multiple R-squared: 0.7187,     Adjusted R-squared: 0.707
F-statistic:  61.2 on 20 and 479 DF,  p-value: < 2.2e-16


The first 4 variables are “highly significant” and the 5th is borderline.

#### Saturated model

We can fit the saturated model using JAGS with the following code.

require(rjags)
data=list(y=y,X=X,n=n,p=p)
init=list(tau=1,alpha=0,beta=rep(0,p))
modelstring="
model {
for (i in 1:n) {
mean[i]<-alpha+inprod(X[i,],beta)
y[i]~dnorm(mean[i],tau)
}
for (j in 1:p) {
beta[j]~dnorm(0,0.001)
}
alpha~dnorm(0,0.0001)
tau~dgamma(1,0.001)
}
"
model=jags.model(textConnection(modelstring),
data=data,inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("alpha","beta","tau"),
n.iter=10000,thin=1)
print(summary(output))
plot(output)


I’ve hard-coded various hyper-parameters in the script which are vaguely reasonable for this kind of problem. I won’t include all of the output in this post, but this works fine and gives sensible results. However, it does not address the variable selection problem.

#### Basic variable selection

Let’s now modify the above script to do basic variable selection in the style of Kuo and Mallick.

data=list(y=y,X=X,n=n,p=p)
init=list(tau=1,alpha=0,betaT=rep(0,p),ind=rep(0,p))
modelstring="
model {
for (i in 1:n) {
mean[i]<-alpha+inprod(X[i,],beta)
y[i]~dnorm(mean[i],tau)
}
for (j in 1:p) {
ind[j]~dbern(0.2)
betaT[j]~dnorm(0,0.001)
beta[j]<-ind[j]*betaT[j]
}
alpha~dnorm(0,0.0001)
tau~dgamma(1,0.001)
}
"
model=jags.model(textConnection(modelstring),
data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,
variable.names=c("alpha","beta","ind","tau"),
n.iter=10000,thin=1)
print(summary(output))
plot(output)


Note that I’ve hard-coded an expectation that around 20% of variables should be included in the model. Again, I won’t include all of the output here, but the posterior mean of the indicator variables can be interpreted as posterior probabilities that the variables should be included in the model. Inspecting the output then reveals that the first three variables have a posterior probability of very close to one, the 4th variable has a small but non-negligible probability of inclusion, and the other variables all have very small probabilities of inclusion.

This is fine so far as it goes, but is not entirely satisfactory. One problem is that the choice of a “fixed effects” prior for the regression coefficients of the included variables is likely to lead to a Lindley’s paradox type situation, and a consequent under-selection of variables. It is arguably better to model the distribution of included variables using a “random effects” approach, leading to a more appropriate distribution for the included variables.

#### Variable selection with random effects

Adopting a random effects distribution for the included coefficients that is normal with mean zero and unknown variance helps to combat Lindley’s paradox, and can be implemented as follows.

data=list(y=y,X=X,n=n,p=p)
init=list(tau=1,taub=1,alpha=0,betaT=rep(0,p),ind=rep(0,p))
modelstring="
model {
for (i in 1:n) {
mean[i]<-alpha+inprod(X[i,],beta)
y[i]~dnorm(mean[i],tau)
}
for (j in 1:p) {
ind[j]~dbern(0.2)
betaT[j]~dnorm(0,taub)
beta[j]<-ind[j]*betaT[j]
}
alpha~dnorm(0,0.0001)
tau~dgamma(1,0.001)
taub~dgamma(1,0.001)
}
"
model=jags.model(textConnection(modelstring),
data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,
variable.names=c("alpha","beta","ind","tau","taub"),
n.iter=10000,thin=1)
print(summary(output))
plot(output)


This leads to a large inclusion probability for the 4th variable, and non-negligible inclusion probabilities for the next few (it is obviously somewhat dependent on the simulated data set). This random effects variable selection modelling approach generally performs better, but it still has the potentially undesirable feature of hard-coding the probability of variable inclusion. Under the prior model, the number of variables included is binomial, and the binomial distribution is rather concentrated about its mean. Where there is a general desire to control the degree of sparsity in the model, this is a good thing, but if there is considerable uncertainty about the degree of sparsity that is anticipated, then a more flexible model may be desirable.

#### Variable selection with random effects and a prior on the inclusion probability

The previous model can be modified by introducing a Beta prior for the model inclusion probability. This induces a distribution for the number of included variables which has longer tails than the binomial distribution, allowing the model to learn about the degree of sparsity.

data=list(y=y,X=X,n=n,p=p)
init=list(tau=1,taub=1,pind=0.5,alpha=0,betaT=rep(0,p),ind=rep(0,p))
modelstring="
model {
for (i in 1:n) {
mean[i]<-alpha+inprod(X[i,],beta)
y[i]~dnorm(mean[i],tau)
}
for (j in 1:p) {
ind[j]~dbern(pind)
betaT[j]~dnorm(0,taub)
beta[j]<-ind[j]*betaT[j]
}
alpha~dnorm(0,0.0001)
tau~dgamma(1,0.001)
taub~dgamma(1,0.001)
pind~dbeta(2,8)
}
"
model=jags.model(textConnection(modelstring),
data=data,inits=init)
update(model,n.iter=1000)
output=coda.samples(model=model,
variable.names=c("alpha","beta","ind","tau","taub","pind"),
n.iter=10000,thin=1)
print(summary(output))
plot(output)


It turns out that for this particular problem the posterior distribution is not very different to the previous case, as for this problem the hard-coded choice of 20% is quite consistent with the data. However, the variable inclusion probabilities can be rather sensitive to the choice of hard-coded proportion.

#### Conclusion

Bayesian variable selection (and model selection more generally) is a very delicate topic, and there is much more to say about it. In this post I’ve concentrated on the practicalities of introducing variable selection into JAGS models. For further reading, I highly recommend the review of O’Hara and Sillanpaa (2009), which discusses other computational algorithms for variable selection. I intend to discuss some of the other methods in future posts.

#### References

O’Hara, R. and Sillanpaa, M. (2009) A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis, 4(1):85-118. [DOI, PDF, Supp, BUGS Code]
Kuo, L. and Mallick, B. (1998) Variable selection for regression models. Sankhya B, 60(1):65-81.

## Inlining JAGS models in R scripts for rjags

JAGS (Just Another Gibbs Sampler) is a general purpose MCMC engine similar to WinBUGS and OpenBUGS. I have a slight preference for JAGS as it is free and portable, works well on Linux, and interfaces well with R. It is tempting to write a tutorial introduction to JAGS and the corresponding R package, rjags, but there is a lot of material freely available on-line already, so it isn’t really necessary. If you are new to JAGS, I suggest starting with Getting Started with JAGS, rjags, and Bayesian Modelling. In this post I want to focus specifically on the problem of inlining JAGS models in R scripts as it can be very useful, and is usually skipped in introductory material.

#### JAGS and rjags on Ubuntu Linux

On recent versions of Ubuntu, assuming that R is already installed, the simplest way to install JAGS and rjags is using the command

sudo apt-get install jags r-cran-rjags


Now rjags is a CRAN package, so it can be installed in the usual way with install.packages("rjags"). However, taking JAGS and rjags direct from the Ubuntu repos should help to ensure that the versions of JAGS and rjags are in sync, which is a good thing.

#### Toy model

For this post, I will use a trivial toy example of inference for the mean and precision of a normal random sample. That is, we will assume data

$X_i \sim N(\mu,1/\tau),\quad i=1,2,\ldots n,$

with priors on $\mu$ and $\tau$ of the form

$\tau\sim Ga(a,b),\quad \mu \sim N(c,1/d).$

#### Separate model file

The usual way to fit this model in R using rjags is to first create a separate file containing the model

  model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}


Then, supposing that this file is called jags1.jags, an R session to fit the model could be constructed as follows:

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
model=jags.model("jags1.jags",data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


This is all fine, and it can be very useful to have the model declared in a separate file, especially if the model is large and complex, and you might want to use it from outside R. However, very often for simple models it can be quite inconvenient to have the model separate from the R script which runs it. In particular, people often have issues with naming files correctly, making sure R is looking in the correct directory, moving the model with the R script, etc. So it would be nice to be able to just inline the JAGS model within an R script, to keep the model, the data, and the analysis all together in one place.

#### Using a temporary file

What we want to do is declare the JAGS model within a text string inside an R script and then somehow pass this into the call to jags.model(). The obvious way to do this is to write the string to a text file, and then pass the name of that text file into jags.model(). This works fine, but some care needs to be taken to make sure this works in a generic platform independent way. For example, you need to write to a file that you know doesn’t exist in a directory that is writable using a filename that is valid on the OS on which the script is being run. For this purpose R has an excellent little function called tempfile() which solves exactly this naming problem. It should always return the name of a file which does not exist in a writable directly within the standard temporary file location on the OS on which R is being run. This function is exceedingly useful for all kinds of things, but doesn’t seem to be very well known by newcomers to R. Using this we can construct a stand-alone R script to fit the model as follows:

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
modelstring="
model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}
"
tmpf=tempfile()
tmps=file(tmpf,"w")
cat(modelstring,file=tmps)
close(tmps)
model=jags.model(tmpf,data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


Now, although there is a file containing the model temporarily involved, the script is stand-alone and portable.

#### Using a text connection

The solution above works fine, but still involves writing a file to disk and reading it back in again, which is a bit pointless in this case. We can solve this by using another under-appreciated R function, textConnection(). Many R functions which take a file as an argument will work fine if instead passed a textConnection object, and the rjags function jags.model() is no exception. Here, instead of writing the model string to disk, we can turn it into a textConnection object and then pass that directly into jags.model() without ever actually writing the model file to disk. This is faster, neater and cleaner. An R session which takes this approach is given below.

require(rjags)
x=rnorm(15,25,2)
data=list(x=x,n=length(x))
hyper=list(a=3,b=11,cc=10,d=1/100)
init=list(mu=0,tau=1)
modelstring="
model {
for (i in 1:n) {
x[i]~dnorm(mu,tau)
}
mu~dnorm(cc,d)
tau~dgamma(a,b)
}
"
model=jags.model(textConnection(modelstring), data=append(data,hyper), inits=init)
update(model,n.iter=100)
output=coda.samples(model=model,variable.names=c("mu", "tau"), n.iter=10000, thin=1)
print(summary(output))
plot(output)


This is my preferred way to use rjags. Note again that textConnection objects have many and varied uses and applications that have nothing to do with rjags.