Bayesian inference for a logistic regression model (Part 6)

Part 6: Hamiltonian Monte Carlo (HMC)


This is the sixth part in a series of posts on MCMC-based Bayesian inference for a logistic regression model. If you are new to this series, please go back to Part 1.

In the previous post we saw how to construct an MCMC algorithm utilising gradient information by considering a Langevin equation having our target distribution of interest as its equilibrium. This equation has a physical interpretation in terms of the stochastic dynamics of a particle in a potential equal to minus the log of the target density. It turns out that thinking about the deterministic dynamics of a particle in such a potential can lead to more efficient MCMC algorithms.

Hamiltonian dynamics

Hamiltonian dynamics is often presented as an extension of a fairly general version of Lagrangian dynamics. However, for our purposes a rather simple version is quite sufficient, based on basic concepts from Newtonian dynamics, familiar from school. Inspired by our Langevin example, we will consider the dynamics of a particle in a potential function V(q). We will see later why we want V(q) = -\log \pi(q) for our target of interest, \pi(\cdot). In the context of Hamiltonian (and Lagrangian) dynamics we typically use q as our position variable, rather than x.

The potential function induces a (conservative) force on the particle equal to -\nabla V(q) when the particle is at position q. Then Newton’s second law of motion, "F=ma", takes the form

\displaystyle \nabla V(q) + m \ddot{q} = 0.

In Newtonian mechanics, we often consider the position vector q as 3-dimensional. Here it will be n-dimensional, where n is the number of variables in our target. We can then think of our second law as governing a single n-dimensional particle of mass m, or n one-dimensional particles all of mass m. But in this latter case, there is no need to assume that all particles have the same mass, and we could instead write our law of motion as

\displaystyle \nabla V(q) + M \ddot{q} = 0,

where M is a diagonal matrix. But in fact, since we could change coordinates, there’s no reason to require that M is diagonal. All we need is that M is positive definite, so that we don’t have negative mass in any coordinate direction.

We will take the above equation as the fundamental law governing our dynamical system of interest. The motivation from Newtonian dynamics is interesting, but not required. What is important is that the dynamics of such a system are conservative, in a way that we will shortly make precise.

Our law of motion is a second-order differential equation, since it involves the second derivative of q wrt time. If you’ve ever studied differential equations, you’ll know that there is an easy way to turn a second order equation into a first order equation with twice the dimension by augmenting the system with the velocities. Here, it is more convenient to augment the system with "momentum" variables, p, which we define as p = M\dot{q}. Then we can write our second order system as a pair of first order equations

\displaystyle \dot{q} = M^{-1}p

\displaystyle \dot{p} = -\nabla V(q)

These are, in fact, Hamilton’s equations for this system, though this isn’t how they are typically written.

If we define the kinetic energy as

\displaystyle T(p) = \frac{1}{2}p^\text{T}M^{-1}p,

then the Hamiltonian

\displaystyle H(q,p) = V(q) + T(p),

representing the total energy in the system, is conserved, since

\displaystyle \dot{H} = \nabla V\cdot \dot{q} + \dot{p}^\text{T}M^{-1}p = \nabla V\cdot \dot{q} + \dot{p}^\text{T}\dot{q} = [\nabla V + \dot{p}]\cdot\dot{q} = 0.

So, if we obey our Hamiltonian dynamics, our trajectory in (q,p)-space will follow contours of the Hamiltonian. It’s also clear that the system is time-reversible, so flipping the sign of the momentum p and integrating will exactly reverse the direction in which the contours are traversed. Another quite important property of Hamiltonian dynamics is that they are volume preserving. This can be verified by checking that the divergence of the flow is zero.

\displaystyle \nabla\cdot(\dot{q},\dot{p}) = \nabla_q\cdot\dot{q} + \nabla_p\cdot\dot{p} = 0,

since \dot{q} is a function of p only and \dot{p} is a function of q only.

Hamiltonian Monte Carlo (HMC)

In Hamiltonian Monte Carlo we introduce an augmented target distribution,

\displaystyle \tilde \pi(q,p) \propto \exp[-H(q,p)]

It is clear from this definition that moves leaving the Hamiltonian invariant will also leave the augmented target density unchanged. By following the Hamiltonian dynamics, we will be able to make big (reversible) moves in the space that will be accepted with high probability. Also, our target factorises into two independent components as

\displaystyle \tilde \pi(q,p) \propto \exp[-V(q)]\exp[-T(p)],

and so choosing V(q)=-\log \pi(q) will ensure that the q-marginal is our real target of interest, \pi(\cdot). It’s also clear that our p-marginal is \mathcal N(0,M). This is also the full-conditional for p, so re-sampling p from this distribution and leaving q unchanged is a Gibbs move that will leave the augmented target invariant. Re-sampling p will be necessary to properly explore our augmented target, since this will move us to a different contour of H.

So, an idealised version of HMC would proceed as follows: First, update p by sampling from its known tractable marginal. Second, update p and q jointly by following the Hamiltonian dynamics. If this second move is regarded as a (deterministic) reversible M-H proposal, it will be accepted with probability one since it leaves the augmented target density unchanged. If we could exactly integrate Hamilton’s equations, this would be fine. But in practice, we will need to use some imperfect numerical method for the integration step. But just as for MALA, we can regard the numerical method as a M-H proposal and correct for the fact that it is imperfect, preserving the exact augmented target distribution.

Hamiltonian systems admit nice numerical integration schemes called symplectic integrators. In HMC a simple alternating Euler method is typically used, known as the leap-frog algorithm. The component updates are all shear transformations, and therefore volume preserving, and exact reversibility is ensured by starting and ending with a half-step update of the momentum variables. In principle, to ensure reversibility of the proposal the momentum variables should be sign-flipped (reversed) to finish, but in practice this doesn’t matter since it doesn’t affect the evaluation of the Hamiltonian and it will then get refreshed, anyway.

So, advancing our system by a time step \epsilon can be done with

\displaystyle p(t+\epsilon/2) := p(t) - \frac{\epsilon}{2}\nabla V(q(t))

\displaystyle q(t+\epsilon) := q(t) + \epsilon M^{-1}p(t+\epsilon/2)

\displaystyle p(t+\epsilon) := p(t+\epsilon/2) - \frac{\epsilon}{2}\nabla V(q(t+\epsilon))

It is clear that if many such updates are chained together, adjacent momentum updates can be collapsed together, giving rise to the "leap-frog" nature of the algorithm, and therefore requiring roughly one gradient evaluation per \epsilon update, rather than two. Since this integrator is volume preserving and exactly reversible, for reasonably small \epsilon it follows the Hamiltonian dynamics reasonably well, but not exactly, and so it does not exactly preserve the Hamiltonian. However, it does make a good M-H proposal, and reasonable acceptance probabilities can often be obtained by chaining together l updates to advance the time of the system by T=l\epsilon. The "optimal" value of l and \epsilon will be highly problem dependent, but values of l=20 or l=50 are not unusual. There are various more-or-less standard methods for tuning these, but we will not consider them here.

Note that since our HMC update on the augmented space consists of a Gibbs move and a M-H update, it is important that our M-H kernel does not keep or thread through the old log target density from the previous M-H update, since the Gibbs move will have changed it in the meantime.



We need a M-H kernel that does not thread through the old log density.

mhKernel = function(logPost, rprop)
    function(x) {
        prop = rprop(x)
        a = logPost(prop) - logPost(x)
        if (log(runif(1)) < a)

We can then use this to construct a M-H move as part of our HMC update.

hmcKernel = function(lpi, glpi, eps = 1e-4, l=10, dmm = 1) {
    sdmm = sqrt(dmm)
    leapf = function(q, p) {
        p = p + 0.5*eps*glpi(q)
        for (i in 1:l) {
            q = q + eps*p/dmm
            if (i < l)
                p = p + eps*glpi(q)
                p = p + 0.5*eps*glpi(q)
        list(q=q, p=-p)
    alpi = function(x)
        lpi(x$q) - 0.5*sum((x$p^2)/dmm)
    rprop = function(x)
        leapf(x$q, x$p)
    mhk = mhKernel(alpi, rprop)
    function(q) {
        d = length(q)
        x = list(q=q, p=rnorm(d, 0, sdmm))

See the full runnable script for further details.


First a M-H kernel,

def mhKernel(lpost, rprop):
    def kernel(x):
        prop = rprop(x)
        a = lpost(prop) - lpost(x)
        if (np.log(np.random.rand()) < a):
            x = prop
        return x
    return kernel

and then an HMC kernel.

def hmcKernel(lpi, glpi, eps = 1e-4, l=10, dmm = 1):
    sdmm = np.sqrt(dmm)
    def leapf(q, p):    
        p = p + 0.5*eps*glpi(q)
        for i in range(l):
            q = q + eps*p/dmm
            if (i < l-1):
                p = p + eps*glpi(q)
                p = p + 0.5*eps*glpi(q)
        return (q, -p)
    def alpi(x):
        (q, p) = x
        return lpi(q) - 0.5*np.sum((p**2)/dmm)
    def rprop(x):
        (q, p) = x
        return leapf(q, p)
    mhk = mhKernel(alpi, rprop)
    def kern(q):
        d = len(q)
        p = np.random.randn(d)*sdmm
        return mhk((q, p))[0]
    return kern

See the full runnable script for further details.


Again, we want an appropriate M-H kernel,

def mhKernel(lpost, rprop, dprop = jit(lambda new, old: 1.)):
    def kernel(key, x):
        key0, key1 = jax.random.split(key)
        prop = rprop(key0, x)
        ll = lpost(x)
        lp = lpost(prop)
        a = lp - ll + dprop(x, prop) - dprop(prop, x)
        accept = (jnp.log(jax.random.uniform(key1)) < a)
        return jnp.where(accept, prop, x)
    return kernel

and then an HMC kernel.

def hmcKernel(lpi, glpi, eps = 1e-4, l = 10, dmm = 1):
    sdmm = jnp.sqrt(dmm)
    def leapf(q, p):    
        p = p + 0.5*eps*glpi(q)
        for i in range(l):
            q = q + eps*p/dmm
            if (i < l-1):
                p = p + eps*glpi(q)
                p = p + 0.5*eps*glpi(q)
        return jnp.concatenate((q, -p))
    def alpi(x):
        d = len(x) // 2
        return lpi(x[jnp.array(range(d))]) - 0.5*jnp.sum((x[jnp.array(range(d,2*d))]**2)/dmm)
    def rprop(k, x):
        d = len(x) // 2
        return leapf(x[jnp.array(range(d))], x[jnp.array(range(d, 2*d))])
    mhk = mhKernel(alpi, rprop)
    def kern(k, q):
        key0, key1 = jax.random.split(k)
        d = len(q)
        x = jnp.concatenate((q, jax.random.normal(key0, [d])*sdmm))
        return mhk(key1, x)[jnp.array(range(d))]
    return kern

There is something a little bit strange about this implementation, since the proposal for the M-H move is deterministic, the function rprop just ignores the RNG key that is passed to it. We could tidy this up by making a M-H function especially for deterministic proposals. We won’t pursue this here, but this issue will crop up again later in some of the other functional languages.

See the full runnable script for further details.


A M-H kernel,

def mhKern[S](
    logPost: S => Double, rprop: S => S,
    dprop: (S, S) => Double = (n: S, o: S) => 1.0
  ): (S) => S =
    val r = Uniform(0.0,1.0)
    x0 =>
      val x = rprop(x0)
      val ll0 = logPost(x0)
      val ll = logPost(x)
      val a = ll - ll0 + dprop(x0, x) - dprop(x, x0)
      if (math.log(r.draw()) < a) x else x0

and a HMC kernel.

def hmcKernel(lpi: DVD => Double, glpi: DVD => DVD, dmm: DVD,
  eps: Double = 1e-4, l: Int = 10) =
  val sdmm = sqrt(dmm)
  def leapf(q: DVD, p: DVD): (DVD, DVD) = 
    @tailrec def go(q0: DVD, p0: DVD, l: Int): (DVD, DVD) =
      val q = q0 + eps*(p0/:/dmm)
      val p = if (l > 1)
        p0 + eps*glpi(q)
        p0 + 0.5*eps*glpi(q)
      if (l == 1)
        (q, -p)
        go(q, p, l-1)
    go(q, p + 0.5*eps*glpi(q), l)
  def alpi(x: (DVD, DVD)): Double =
    val (q, p) = x
    lpi(q) - 0.5*sum(pow(p,2) /:/ dmm)
  def rprop(x: (DVD, DVD)): (DVD, DVD) =
    val (q, p) = x
    leapf(q, p)
  val mhk = mhKern(alpi, rprop)
  (q: DVD) =>
    val d = q.length
    val p = sdmm map (sd => Gaussian(0,sd).draw())
    mhk((q, p))._1

See the full runnable script for further details.


A M-H kernel:

mdKernel :: (StatefulGen g m) => (s -> Double) -> (s -> s) -> g -> s -> m s
mdKernel logPost prop g x0 = do
  let x = prop x0
  let ll0 = logPost x0
  let ll = logPost x
  let a = ll - ll0
  u <- (genContVar (uniformDistr 0.0 1.0)) g
  let next = if ((log u) < a)
        then x
        else x0
  return next

Note that here we are using a M-H kernel specifically for deterministic proposals, since there is no non-determinism signalled in the type signature of prop. We can then use this to construct our HMC kernel.

hmcKernel :: (StatefulGen g m) =>
  (Vector Double -> Double) -> (Vector Double -> Vector Double) -> Vector Double ->
  Double -> Int -> g ->
  Vector Double -> m (Vector Double)
hmcKernel lpi glpi dmm eps l g = let
  sdmm = cmap sqrt dmm
  leapf q p = let
    go q0 p0 l = let
      q = q0 + (scalar eps)*p0/dmm
      p = if (l > 1)
        then p0 + (scalar eps)*(glpi q)
        else p0 + (scalar (eps/2))*(glpi q)
      in if (l == 1)
      then (q, -p)
      else go q p (l - 1)
    in go q (p + (scalar (eps/2))*(glpi q)) l
  alpi x = let
    (q, p) = x
    in (lpi q) - 0.5*(sumElements (p*p/dmm))
  prop x = let
    (q, p) = x
    in leapf q p
  mk = mdKernel alpi prop g
  in (\q0 -> do
         let d = size q0
         zl <- (replicateM d . genContVar (normalDistr 0.0 1.0)) g
         let z = fromList zl
         let p0 = sdmm * z
         (q, p) <- mk (q0, p0)
         return q)

See the full runnable script for further details.


Again we can use a M-H kernel specific to deterministic proposals.

def mdKernel {s} (lpost: s -> Float) (prop: s -> s)
    (x0: s) (k: Key) : s =
  x = prop x0
  ll0 = lpost x0
  ll = lpost x
  a = ll - ll0
  u = rand k
  select (log u < a) x x0

and use this to construct an HMC kernel.

def hmcKernel {n} (lpi: (Fin n)=>Float -> Float)
    (dmm: (Fin n)=>Float) (eps: Float) (l: Nat)
    (q0: (Fin n)=>Float) (k: Key) : (Fin n)=>Float =
  sdmm = sqrt dmm
  idmm = map (\x. 1.0/x) dmm
  glpi = grad lpi
  def leapf (q0: (Fin n)=>Float) (p0: (Fin n)=>Float) :
      ((Fin n)=>Float & (Fin n)=>Float) =
    p1 = p0 + (eps/2) .* (glpi q0)
    q1 = q0 + eps .* (p1*idmm)
    (q, p) = apply_n l (q1, p1) \(qo, po).
      pn = po + eps .* (glpi qo)
      qn = qo + eps .* (pn*idmm)
      (qn, pn)
    pf = p + (eps/2) .* (glpi q)
    (q, -pf)
  def alpi (qp: ((Fin n)=>Float & (Fin n)=>Float)) : Float =
    (q, p) = qp
    (lpi q) - 0.5*(sum (p*p*idmm))
  def prop (qp: ((Fin n)=>Float & (Fin n)=>Float)) :
      ((Fin n)=>Float & (Fin n)=>Float) =
    (q, p) = qp
    leapf q p
  mk = mdKernel alpi prop
  [k1, k2] = split_key k
  z = randn_vec k1
  p0 = sdmm * z
  (q, p) = mk (q0, p0) k2

Note that the gradient is obtained via automatic differentiation. See the full runnable script for details.

Next steps

This was the main place that I was trying to get to when I started this series of posts. For differentiable log-posteriors (as we have in the case of Bayesian logistic regression), HMC is a pretty good algorithm for reasonably efficient posterior exploration. But there are lots of places we could go from here. We could explore the tuning of MCMC algorithms, or HMC extensions such as NUTS. We could look at MCMC algorithms that are specifically tailored to the logistic regression problem, or we could look at new MCMC algorithms for differentiable targets based on piecewise deterministic Markov processes. Alternatively, we could temporarily abandon MCMC and look at SMC or ABC approaches. Another possibility would be to abandon this multi-language approach and have a bit of a deep dive into Dex, which I think has the potential to be a great programming language for statistical computing. All of these are possibilities for the future, but I’ve a busy few weeks coming up, so the frequency of these posts is likely to substantially decrease.

Remember that all of the code associated with this series of posts is available from this github repo.


Bayesian hierarchical modelling with Rainier


In the previous post I gave a brief introduction to Rainier, a new HMC-based probabilistic programming library/DSL for Scala. In that post I assumed that people were using the latest source version of the library. Since then, version 0.1.1 of the library has been released, so in this post I will demonstrate use of the released version of the software (using the binaries published to Sonatype), and will walk through a slightly more interesting example – a dynamic linear state space model with unknown static parameters. This is similar to, but slightly different from, the DLM example in the Rainier library. So to follow along with this post, all that is required is SBT.

An interactive session

First run SBT from an empty directory, and paste the following at the SBT prompt:

set libraryDependencies  += "com.stripe" %% "rainier-plot" % "0.1.1"
set scalaVersion := "2.12.4"

This should give a Scala REPL with appropriate dependencies (rainier-plot has all of the relevant transitive dependencies). We’ll begin with some imports, and then simulating some synthetic data from a dynamic linear state space model with an AR(1) latent state and Gaussian noise on the observations.

import com.stripe.rainier.compute._
import com.stripe.rainier.core._
import com.stripe.rainier.sampler._

implicit val rng = ScalaRNG(1)
val n = 60 // number of observations/time points
val mu = 3.0 // AR(1) mean
val a = 0.95 // auto-regressive parameter
val sig = 0.2 // AR(1) SD
val sigD = 3.0 // observational SD
val state = Stream.
  iterate(0.0)(x => mu + (x - mu) * a + sig * rng.standardNormal).
val obs = + sigD * rng.standardNormal)

Now we have some synthetic data, let’s think about building a probabilistic program for this model. Start with a prior.

case class Static(mu: Real, a: Real, sig: Real, sigD: Real)
val prior = for {
  mu <- Normal(0, 10).param
  a <- Normal(1, 0.1).param
  sig <- Gamma(2,1).param
  sigD <- Gamma(2,2).param
  sp <- Normal(0, 50).param
} yield (Static(mu, a, sig, sigD), List(sp))

Note the use of a case class for wrapping the static parameters. Next, let’s define a function to add a state and associated observation to an existing model.

def addTimePoint(current: RandomVariable[(Static, List[Real])],
                     datum: Double) = for {
  tup <- current
  static = tup._1
  states = tup._2
  os = states.head
  ns <- Normal((( - static.a) * + (static.a * os),
  _ <- Normal(ns, static.sigD).fit(datum)
} yield (static, ns :: states)

Given this, we can generate the probabilistic program for our model as a fold over the data initialised with the prior.

val fullModel = obs.foldLeft(prior)(addTimePoint(_, _))

If we don’t want to keep samples for all of the variables, we can focus on the parameters of interest, wrapping the results in a Map for convenient sampling and plotting.

val model = for {
  tup <- fullModel
  static = tup._1
  states = tup._2
} yield
  Map("mu" ->,
  "a" -> static.a,
  "sig" -> static.sig,
  "sigD" -> static.sigD,
  "SP" -> states.reverse.head)

We can sample with

val out = model.sample(HMC(3), 100000, 10000 * 500, 500)

(this will take several minutes) and plot some diagnostics with

import com.cibo.evilplot.geometry.Extent
import com.stripe.rainier.plot.EvilTracePlot._

val truth = Map("mu" -> mu, "a" -> a, "sigD" -> sigD,
  "sig" -> sig, "SP" -> state(0))
render(traces(out, truth), "traceplots.png",
  Extent(1200, 1400))
render(pairs(out, truth), "pairs.png")

This generates the following diagnostic plots:

Everything looks good.


Rainier is a monadic embedded DSL for probabilistic programming in Scala. We can use standard functional combinators and for-expressions for building models to sample, and then run an efficient HMC algorithm on the resulting probability monad in order to obtain samples from the posterior distribution of the model.

See the Rainier repo for further details.

Monadic probabilistic programming in Scala with Rainier


Rainier is an interesting new probabilistic programming library for Scala recently open-sourced by Stripe. Probabilistic programming languages provide a computational framework for building and fitting Bayesian models to data. There are many interesting probabilistic programming languages, and there is currently a lot of interesting innovation happening with probabilistic programming languages embedded in strongly typed functional programming languages such as Scala and Haskell. However, most such languages tend to be developed by people lacking expertise in statistics and numerics, leading to elegant, composable languages which work well for toy problems, but don’t scale well to the kinds of practical problems that applied statisticians are interested in. Conversely, there are a few well-known probabilistic programming languages developed by and for statisticians which have efficient inference engines, but are hampered by inflexible, inelegant languages and APIs. Rainier is interesting because it is an attempt to bridge the gap between these two worlds: it has a functional, composable, extensible, monadic API, yet is backed by a very efficient, high-performance scalable inference engine, using HMC and a static compute graph for reverse-mode AD. Clearly there will be some loss of generality associated with choosing an efficient inference algorithm (eg. for HMC, there needs to be a fixed number of parameters and they must all be continuous), but it still covers a large proportion of the class of hierarchical models commonly used in applied statistical modelling.

In this post I’ll give a quick introduction to Rainier using an interactive session requiring only that SBT is installed and the Rainier repo is downloaded or cloned.

Interactive session

To follow along with this post just clone, or download and unpack, the Rainier repo, and run SBT from the top-level Rainier directory and paste commands. First start a Scala REPL.

project rainierPlot

Before we start building models, we need some data. For this post we will focus on a simple logistic regression model, and so we will begin by simulating some synthetic data consistent with such a model.

val r = new scala.util.Random(0)
val N = 1000
val beta0 = 0.1
val beta1 = 0.3
val x = (1 to N) map { i =>
  3.0 * r.nextGaussian
val theta = x map { xi =>
  beta0 + beta1 * xi
def expit(x: Double): Double = 1.0 / (1.0 + math.exp(-x))
val p = theta map expit
val y = p map (pi => (r.nextDouble < pi))

Now we have some synthetic data, we can fit the model and see if we are able to recover the “true” parameters used to generate the synthetic data. In Rainier, we build models by declaring probabilistic programs for the model and the data, and then run an inference engine to generate samples from the posterior distribution.

Start with a bunch of Rainier imports:

import com.stripe.rainier.compute._
import com.stripe.rainier.core._
import com.stripe.rainier.sampler._
import com.stripe.rainier.repl._

Now we want to build a model. We do so by describing the joint distribution of parameters and data. Rainier has a few built-in distributions, and these can be combined using standard functional monadic combinators such as map, zip, flatMap, etc., to create a probabilistic program representing a probability monad for the model. Due to the monadic nature of such probabilistic programs, it is often most natural to declare them using a for-expression.

val model = for {
  beta0 <- Normal(0, 5).param
  beta1 <- Normal(0, 5).param
  _ <- Predictor.from{x: Double =>
        val theta = beta0 + beta1 * x
        val p = Real(1.0) / (Real(1.0) + (Real(0.0) - theta).exp)
    }.fit(x zip y)
} yield Map("b0"->beta0, "b1"->beta1)

This kind of construction is very natural for anyone familiar with monadic programming in Scala, but will no doubt be a little mysterious otherwise. RandomVariable is the probability monad used for HMC sampling, and these can be constructed from Distributions using .param (for unobserved parameters) and .fit (for variables with associated observations). Predictor is just a convenience for observations corresponding to covariate information. model is therefore a RandomVariable over beta0 and beta1, the two unobserved parameters of interest. Note that I briefly discussed this kind of pure functional approach to describing probabilistic programs (using Rand from Breeze) in my post on MCMC as a stream.

Now we have our probabilistic program, we can sample from it using HMC as follows.

implicit val rng = ScalaRNG(3)
val its = 10000
val thin = 5
val out = model.sample(HMC(5), 10000, its*thin, thin)

The argument to HMC() is the number of leapfrog steps to take per iteration.

Finally, we can use EvilPlot to look at the HMC output and check that we have managed to reasonably recover the true parameters associated with our synthetic data.

import com.cibo.evilplot.geometry.Extent
import com.stripe.rainier.plot.EvilTracePlot._

render(traces(out, truth = Map("b0" -> beta0, "b1" -> beta1)),
  "traceplots.png", Extent(1200, 1000))
render(pairs(out, truth = Map("b0" -> beta0, "b1" -> beta1)), "pairs.png")

Everything looks good, and the sampling is very fast!

Further reading

For further information, see the Rainier repo. In particular, start with the tour of Rainier’s core, which gives a more detailed introduction to how Rainier works than this post. Those interested in how the efficient AD works may want to read about the compute graph, and the implementation notes explain how it all fits together. There is some basic ScalaDoc for the core package, and also some examples (including this one), and there’s a gitter channel for asking questions. This is a very new project, so there are a few minor bugs and wrinkles in the initial release, but development is progressing rapidly, so I fully expect the library to get properly battle-hardened over the next few months.

For those unfamiliar with the monadic approach to probabilistic programming, then Ścibior et al (2015) is probably a good starting point.