# Bayesian inference for a logistic regression model (Part 6)

## Part 6: Hamiltonian Monte Carlo (HMC)

### Introduction

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.

## Implementations

### R

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)
prop
else
x
}


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)
else
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))
mhk(x)\$q
}
}


See the full runnable script for further details.

### Python

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)
else:
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.

#### JAX

Again, we want an appropriate M-H kernel,

def mhKernel(lpost, rprop, dprop = jit(lambda new, old: 1.)):
@jit
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)
@jit
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)
else:
p = p + 0.5*eps*glpi(q)
return jnp.concatenate((q, -p))
@jit
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)
@jit
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)
@jit
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.

### Scala

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)
else
p0 + 0.5*eps*glpi(q)
if (l == 1)
(q, -p)
else
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.

### Dex

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
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
q


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.

### darrenjw

I am Professor of Statistics within the Department of Mathematical Sciences at Durham University, UK. I am an Bayesian statistician interested in computation and applications, especially to engineering and the life sciences.

## One thought on “Bayesian inference for a logistic regression model (Part 6)”

1. Vangelis says:

Thank for presenting the mathematical details in an accessible manner and with accompanying code in different languages. It was interesting to see the various implementations.

I would like to suggest that you follow with a part on manifold Langevin and Hamiltonian Monte-Carlo methods of Girolami and Calderhead, J.R.Statist. Soc. B (2011). Section 7 in that paper applies the aforementioned methods to the Bayesian logistic regression model.

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