Why going for Hamilton Monte Carlo

Especially convenient for Bayesians \[ \pi(\theta) \propto \mathrm{prior}(\theta) \times \mathrm{Likelihood}(\theta)\] but also useful for frequentists working on latent class models with SEM based algorithms.

A word of warning:

He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may be cast, L. Da Vinci

…, although most friends of mine (with the exception of some Britons, maybe) would go and sail, blindly relying on modern GPS.

Trying to see farther by standing on giants’shoulders:

Much of the following material has been stolen from:

Hamiltonian Agenda


Gravity : Newton ’s falling apple

With the dot notation for derivatives:

\[\begin{align} \vec{f} & =m\vec{\gamma}\\ mg & =m\ddot{x}\\ mg\dot{x} & =m\ddot{x}\dot{x}\\ 0 & =\frac{d}{dt}(-mgx+\frac{1}{2}mv^{2}) \end{align}\]

\[ \underset{\text{Energy}}{\underbrace{H(v,x)}}=\underset{\text{Potential}% }{\underbrace{U(x)}}+\underset{\text{Cinetic}}{\underbrace{K(v)}} \]

Coiled spring : oscillations

\[\begin{align*} f & =m\gamma\\ -kx & =m\ddot{x}\\ -kx\dot{x} & =m\ddot{x}\dot{x}\\ 0 & =\frac{d}{dt}(\frac{1}{2}mx^{2}+\frac{1}{2}mv^{2}) \end{align*}\]

\[ \underset{\text{Energy}}{\underbrace{H(v,x)}}=\underset{\text{Potential}% }{\underbrace{U(x)}}+\underset{\text{Cinetic}}{\underbrace{K(v)}} \]

More complex systems in physics obbey Hamilton equations

Usually, the total energy is written as:

\[\underset{\text{Energy}}{\underbrace{H(q,p)}}=\underset{\text{Potential}% }{\underbrace{U(q)}}+\underset{\text{Cinetic}}{\underbrace{K(p)}}\]

\(p\) : generalized momentum vector \(\in\mathbb{R}^{d}\)

\(q\) : generalized position vector \(\in\mathbb{R}^{d}\)

\[\begin{align} \frac{dq}{dt} & =\frac{\partial H(q,p)}{\partial p}\\ \frac{dp}{dt} & =-\frac{\partial H(q,p)}{\partial q} \end{align}\]

From Physics to Maths: Hamiltonian properties

What are the properties of a function \(H(p,q)\)… such that:

\[\begin{align*} \frac{dq}{dt} & =\frac{\partial H(q,p)}{\partial p}\\ \frac{dp}{dt} & =-\frac{\partial H(q,p)}{\partial q}% \end{align*}\]

More globally, in just a single equation:

\[\frac{dz}{dt}=J\times\nabla H\]

with \(z=\left(\begin{array}[c]{c} q\\ p \end{array} \right)\), \(J=\left(\begin{array}[c]{cc} 0_{d\times d} & 1_{d\times d}\\ -1_{d\times d} & 0_{d\times d} \end{array} \right)\)

Hamiltonian property 1 : Reversibility

The mapping \(\mathcal{T}_{s}\) : \(\left( t,\left( \begin{array}[c]{c} q(t)\\ p(t) \end{array} \right) \right) \longmapsto\left( t+s,\left( \begin{array}[c]{c} q(t+s)\\ p(t+s) \end{array}\right)\right)\) is one-to-one, thus reversible.

If \(H(q,p)=U(q)+K(p)\) with \(K(p)=K(-p)\), \(\mathcal{T}_{-s}\) is obtained by the sequence of operations \((p\rightarrow-p),\mathcal{T}_{s},(p\rightarrow-p)\)

Hamiltonian property 2 : Conservation of \(H\) along the trajectories of \(\mathcal{T}\)

\[\begin{align*} \frac{dH}{dt} & =\left( \frac{\partial H(q,p)}{\partial p}\right) \frac {dp}{dt}+\left( \frac{\partial H(q,p)}{\partial q}\right) \frac{dq}{dt}\\ & =\frac{\partial H(q,p)}{\partial p}\left( -\frac{\partial H(q,p)}{\partial q}\right) +\frac{\partial H(q,p)}{\partial q}\left( \frac{\partial H(q,p)}{\partial p}\right) \\ & =0 \end{align*}\]

Hamiltonian property 3 : Volume preservation along the trajectories of \(\mathcal{T}\)

A vector field \(\mathcal{T}\) with \(0\) divergence preserves volume:

\[\begin{align*} \Delta\mathcal{T} & =\left( \frac{\partial}{\partial p}\right) \frac {dp}{dt}+\left( \frac{\partial}{\partial q}\right) \frac{dq}{dt}\\ & =\frac{\partial}{\partial p}\left( -\frac{\partial H(q,p))}{\partial q}\right) +\frac{\partial}{\partial q}\left( \frac{\partial H(q,p)}{\partial p}\right) \\ & =-\left( \frac{{\partial^{2}}H(q,p)}{{\partial p}{\partial q}}\right) +\left( \frac{{\partial^{2}}H(q,p)}{{\partial p}{\partial q}}\right) =0 \end{align*}\]

i.e. preservation of pdf by a change of variables since the determinant of the Jacobian matrix \(J_{\delta}\) for a change of coordinates \(\mathcal{T}_{\delta}\) from \(s\) to \(s+\delta\) will be \(1\)!

Numerical integration along the trajectories of \(\mathcal{T}\)

How to practically evaluate \(\mathcal{T}_{\delta}\left(\begin{array}[c]{c} q(t)\\ p(t) \end{array}\right)\) for a small \(\delta\)?

Euler’s discretisation

\[\begin{align*} \left(\begin{array}[c]{c} q(t+\delta)\\ p(t+\delta) \end{array}\right) & \approx\left(\begin{array}[c]{c} q(t)\\ p(t) \end{array}\right) +\delta\left(\begin{array}[c]{c} \frac{dq(t)}{dt}\\ \frac{dp(t)}{dt} \end{array}\right) \\ & =\left(\begin{array}[c]{c} q(t)+\delta\frac{\partial H(q,p)}{\partial p}\\ p(t)-\delta\frac{\partial H(q,p)}{\partial q} \end{array}\right) \end{align*}\]

if \(H(q,p)=U(q)+K(p)\) and \(K(p)=\frac{1}{2}p^{\prime}M^{-1}p\)

\[\begin{align*} \left(\begin{array}[c]{c} q(t+\delta)\\ p(t+\delta) \end{array} \right) & \approx\left( \begin{array}[c]{c} q(t)+\delta\frac{\nabla K(p)}{\partial p}\\ p(t)-\delta\frac{\nabla U(q)}{\partial q} \end{array} \right) \\ & \approx\left( \begin{array}[c]{c} q(t)+\delta\times M^{-1}p(t)\\ p(t)-\delta\times\frac{\partial U}{\partial q}(q(t)) \end{array} \right) \end{align*}\]

Euler’s scheme may encounter problems…Let’s examine the example reproduced from FIG 5.1 of Neal with \(H(q,p)=q^{2}/2+p^{2}/2\) whose trajectories are circles. (The initial state is \(q=0,p=1\). \(L=20\) steps)

Eulerstep = function(epsilon, L, current_q, current_p) {
  Q <- current_q
  P <- current_p
  for (i in 1:L) {
  q = current_q + 1 * current_p * epsilon
  p = current_p - 1 * epsilon * current_q
  current_p = p
  current_q = q
  Q = c(Q, q)
  P = c(P, p)
  return(list(P = P, Q = Q))
angle = seq(0, 2 * pi, length.out = 1000)
plot(cos(angle), sin(angle), lwd = 4, type = 'l', lty = 3, las = 1,
     xlab = "position q",ylab = "position q", xlim = c(-2, 2), ylim = c(-2, 2))
xy = Eulerstep(epsilon = 0.3, L = 20, current_q = 0, current_p = 1)
lines(xy$Q, xy$P, type = "b", pch = 19, col = 'red') 

Euler’s modified discretisation

There are ways to improve the discretisation, for instance Euler’s shear transformation works as follows:

\[\begin{align*} q(t+\delta) & \approx q(t)+\delta\times M^{-1}p(\mathbf{t})\\ p(t+\delta) & \approx p(t)-\delta\times\frac{\partial U}{\partial q}(q(\mathbf{t+\delta})) \end{align*}\]
EulerModifstep = function(epsilon, L, current_q, current_p){
  Q = current_q
  P = current_p
  for (i in 1:L) {
    current_q = current_q + 1 * current_p * epsilon
    current_p = current_p - 1 * epsilon * current_q
    Q = c(Q, current_q)
    P = c(P, current_p)
  return(list(P = P, Q = Q))
plot(cos(angle), sin(angle), lwd = 4, type = 'l', lty = 3, las = 1,
     xlab = "position q", ylab = "position q", xlim = c(-2, 2), ylim = c(-2, 2))
xymod = EulerModifstep(epsilon = 0.3, L = 20, current_q = 0, current_p = 1)
lines(xymod$Q, xymod$P, col = 'blue', type = "b", pch = 19)