PDMP Sampling

Sampling using Piecewise Deterministic Markov Processes

On this website (continuously work in progress) I aim to collect references to papers on the use and analysis of Piecewise Deterministic Markov Processes (PDMPs) for use in MCMC. I will try to list all papers that I find important, will put years of first appearance on arXiv or elsewhere, and list in reverse chronological order. I will highlight papers which in my opinion reflect the current state of the art. Inevitably some of this page will reflect some of my own judgements and prejudice. If you want to bring some paper to my attention, or feel I should correct something, please write me at joris.bierkens AT tudelft.nl.

Joris Bierkens (homepage)

Mathematics of PDMPs

Here papers are listed which concern the mathematical analysis of process underlying sampling by means of PDMPs. I have chosen not to list papers concerning the analysis of PDMPs under assumptions which are not immediately useful for MCMC.

Methodology using PDMPs

Here papers are listed which describe sampling methods based on PDMPs. A concise introduction to the topic may be found in the bold paper.

Statistics literature

Physics literature

As usual many ideas concerning sampling methods originated from physics; these are a few key papers in this respect.

Machine learning literature

Key references on non-reversible MCMC sampling

The use of PDMPs for sampling originated from the attempts to design non-reversible MCMC algorithms. Here I list several key references from the physics, mathematics and statistics literature.

Last update: April 2018