The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data Page 1 The
Annals of Statistics 2019, Vol. 47, No. 3, 1288–1320 https://doi.org/10.1214/18-AOS1715 © …

We study a Markov process with two components: the first component evolves according to
one of finitely many underlying Markovian dynamics, with a choice of dynamics that changes …

Abstract In Turitsyn, Chertkov and Vucelja Phys. D 240 (2011) 410–414 a nonreversible
Markov Chain Monte Carlo (MCMC) method on an augmented state space was introduced …

We give a short overview of recent results on a specific class of Markov process: the
Piecewise Deterministic Markov Processes (PDMPs). We first recall the definition of these …

We provide quantitative bounds for the long time behavior of a class of Piecewise
Deterministic Markov Processes with state space R^d*E where E is a finite set. The …

In this note, we present few examples of Piecewise Deterministic Markov Processes and
their long time behavior. They share two important features: they are related to concrete …

Given an energy potential on the Euclidian space, a piecewise deterministic Markov process
is designed to sample the corresponding Gibbs measure. In dimension one an Eyring …

Markov chain Monte Carlo (MCMC) methods provide an essential tool in statistics for
sampling from complex probability distributions. While the standard approach to MCMC …

We study the long-time behavior of variants of the telegraph process with position-
dependent jump-rates, which result in a monotone gradient-like drift towards the origin. We …

Markov chains and diffusion processes are indispensable tools in machine learning and
statistics that are used for inference, sampling, and modeling. With the growth of large-scale …