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PDMP Monte Carlo methods for piecewise smooth densities

Published online by Cambridge University Press:  12 March 2024

Augustin Chevallier*
Affiliation:
Université de Strasbourg
Sam Power
Affiliation:
University of Bristol
Andi Q. Wang
Affiliation:
University of Warwick
Paul Fearnhead
Affiliation:
Lancaster University
*
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Abstract

There has been substantial interest in developing Markov chain Monte Carlo algorithms based on piecewise deterministic Markov processes. However, existing algorithms can only be used if the target distribution of interest is differentiable everywhere. The key to adapting these algorithms so that they can sample from densities with discontinuities is to define appropriate dynamics for the process when it hits a discontinuity. We present a simple condition for the transition of the process at a discontinuity which can be used to extend any existing sampler for smooth densities, and give specific choices for this transition which work with popular algorithms such as the bouncy particle sampler, the coordinate sampler, and the zigzag process. Our theoretical results extend and make rigorous arguments that have been presented previously, for instance constructing samplers for continuous densities restricted to a bounded domain, and we present a version of the zigzag process that can work in such a scenario. Our novel approach to deriving the invariant distribution of a piecewise deterministic Markov process with boundaries may be of independent interest.

Information

Type
Original Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Applied Probability Trust
Figure 0

Figure 1. Exit boundary $\big(\partial E_0^+\big)$ and entrance boundary $\big(\partial E_0^+$\big): $x_0$ is in the entrance boundary $\partial E_0^-$, while $x'_{\!\!t}$ is in the exit boundary $\partial E_0^+$. The arrows represent the flow $\Phi(\cdot,\cdot)$.

Figure 1

Figure 2. Example trajectories for the bouncy particle sampler (left), coordinate sampler (middle), and zigzag process (right) for simulating from a 100-dimensional Gaussian distribution restricted to a cube for different transitions on the boundary. For the zigzag process and coordinate sampler, the basis is rotated by a random matrix R. We show the dynamics for the first two coordinates only. The different transitions correspond to the limiting behaviour in Sections 6.2.1–6.2.3 (top); using a single Metropolis–Hastings step to sample from $l_x$ (middle); and using 100 Metropolis–Hastings steps to sample from $l_x$ (bottom).

Figure 2

Figure 3. MCMC estimates over time of $\mathbb E_\pi[ 1_{[-1,1]^d}(X)]$, the expectation of the indicator function of $[-1,1]^d$ under $\pi$, for different samplers, repeated 5 times for each sampler. Here $d = 20$ and the expectation is computed with respect to the d-dimensional density (proportional to) $\pi(x) = \alpha_{\mathrm{in}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{in}}^2}} 1_{R^{-1}x\in [-1,1]^d}+\alpha_{\mathrm{out}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{out}}^2}}1_{R^{-1}x\notin [-1,1]^d}$, with $(\alpha_{\mathrm{in}}, \alpha_{\mathrm{out}}, \sigma_{\mathrm{in}}, \sigma_{\mathrm{out}}) = (1,1,2,0.8)$, where R is a random rotation matrix. The red line corresponds to the true value of the expectation. For the bouncy particle sampler, a refresh rate of 5 is used. Each sampler ran for 100000 events.

Figure 3

Figure 4. Example trajectories for the bouncy particle sampler for simulating from a d-dimensional Gaussian distribution restricted to a cube, for $d=2$, 10, 100, and for different transitions on the boundary. For $d=10$, 100 we show the dynamics for the first two coordinates only. The different transitions correspond to the limiting behaviour, $Q_{\mathrm{BPS}}$, of Section 6.2.1 (top); using a single Metropolis–Hastings step to sample from $l_x$ (middle); and using 100 Metropolis–Hastings steps to sample from $l_x$ (bottom).

Figure 4

Figure 5. Example trajectories for the coordinate sampler for simulating from a d-dimensional Gaussian distribution restricted to a cube, for $d=2$, 10, 100, and for different transitions on the boundary. For $d=10$, 100 we show the dynamics for the first two coordinates only. The different transitions correspond to the limiting behaviour, $Q_{\mathrm{CS}}$, of Section 6.2.2 (top); using a single Metropolis–Hastings step to sample from $l_x$ (middle); and using 100 Metropolis–Hastings steps to sample from $l_x$ (bottom).

Figure 5

Figure 6. Example trajectories for the zigzag sampler for simulating from a d-dimensional Gaussian distribution restricted to a cube, for $d=2$, 10, 100, and for different transitions on the boundary. For $d=10$, 100 we show the dynamics for the first two coordinates only. The different transitions correspond to the limiting behaviour, $Q_{\mathrm{ZZ}}$, of Section 6.2.3 (top); using a single Metropolis–Hastings step to sample from $l_x$ (middle); and using 100 Metropolis–Hastings steps to sample from $l_x$ (bottom).

Figure 6

Figure 7. Example trajectories for the zigzag sampler for simulating from a d-dimensional Gaussian distribution restricted to a cube, for $d=2$, 10, 100; and for different transitions on the boundary—using the canonical basis. For $d=10$, 100 we show the dynamics for the first two coordinates only. The different transitions correspond to the limiting behaviour, $Q_{\mathrm{ZZ}}$ of Section 6.2.3 (top); using a single Metropolis–Hastings step to sample from $l_x$ (middle); and using 100 Metropolis–Hastings steps to sample from $l_x$ (bottom).

Figure 7

Figure 8. MCMC estimates over time of $\mathbb E_\pi[ 1_{[-1,1]^d}(X)]$, the expectation of the indicator function of $[-1,1]^d$ under $\pi$, for different samplers, repeated 5 times for each sampler. Here $d = 20$ and the expectation is computed with respect to the d-dimensional density (proportional to) $ \pi(x) = \alpha_{\mathrm{in}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{in}}^2}} 1_{x\in [-1,1]^d}+\alpha_{\mathrm{out}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{out}}^2}}1_{x\notin [-1,1]^d}$, with $(\alpha_{\mathrm{in}}, \alpha_{\mathrm{out}}, \sigma_{\mathrm{in}}, \sigma_{\mathrm{out}}) = (1,1,2,0.8)$. The red line corresponds to the true value. For the bouncy particle sampler, a refresh rate of 5 is used. Each sampler ran for 100000 events.

Figure 8

Figure 9. Example trajectories for each sampler using the 20-dimensional density (proportional to) $\pi(x) = \alpha_{\mathrm{in}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{in}}^2}} 1_{x\in [-1,1]^d}+\alpha_{\mathrm{out}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{out}}^2}}1_{x\notin [-1,1]^d}$, with $(\alpha_{\mathrm{in}}, \alpha_{\mathrm{out}}, \sigma_{\mathrm{in}}, \sigma_{\mathrm{out}}) = (1,1,2,0.8)$. For the bouncy particle sampler, a refresh rate of 5 is used. Each sampler ran for 10000 events.

Figure 9

Figure 10. Example trajectories for each sampler using the 20-dimensional density (proportional to) $\pi(x) = \alpha_{\mathrm{in}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{in}}^2}} 1_{R^{-1}x\in [-1,1]^d}+\alpha_{\mathrm{out}} e^{-\frac{\|x\|^2}{2\sigma_{\mathrm{out}}^2}}1_{R^{-1}x\notin [-1,1]^d}$, with $(\alpha_{\mathrm{in}}, \alpha_{\mathrm{out}}, \sigma_{\mathrm{in}}, \sigma_{\mathrm{out}}) = (1,1,2,0.8)$ and R a random rotation matrix. For the bouncy particle sampler, a refresh rate of 5 is used. Each sampler ran for 10000 events.