Ergodicity of the zigzag process

Abstract

The zigzag process is a Piecewise Deterministic Markov Process which can be used in a MCMC framework to sample from a given target distribution. We prove the convergence of this process to its target under very weak assumptions, and establish a central limit theorem for empirical averages under stronger assumptions on the decay of the target measure. We use the classical "Meyn-Tweedie" approach. The main difficulty turns out to be the proof that the process can indeed reach all the points in the space, even if we consider the minimal switching rates.

References in corpus (5)

• Piecewise-Deterministic Markov Chain Monte Carlo
• Irreversible Langevin samplers and variance reduction: a large deviation approach
• Binary Bouncy Particle Sampler
• Generalized Bouncy Particle Sampler
• Long-time behaviour of generalised Zig-Zag process

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