Abstract Novel Monte Carlo methods to generate samples from a target distribution, such as
a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms …

Monte Carlo simulations of systems of particles such as hard spheres or soft spheres with
singular kernels can display around a phase transition prohibitively long convergence times …

This book aims to provide a graduate-level introduction to advanced topics in Markov chain
Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context …

Recently nonreversible samplers based on simulating piecewise deterministic Markov
processes (PDMPs) have shown potential for efficient sampling in Bayesian inference …

We explore the application of a nonlinear MCMC technique first introduced in [1] to problems
in Bayesian machine learning. We provide a convergence guarantee in total variation that …

We construct a zig-zag process targeting a posterior distribution defined on a hybrid state
space consisting of both discrete and continuous variables. The construction does not …

Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as
datasets grow larger and models grow more complex, many popular MCMC algorithms …

We extend Monte Carlo samplers based on piecewise deterministic Markov processes
(PDMP samplers) by formally defining different boundary conditions such as sticky floors …

Abstract Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics,
and as datasets grow larger and models grow more complex, many popular MCMC …

We introduce novel Markov chain Monte Carlo (MCMC) algorithms based on numerical
approximations of piecewise-deterministic Markov processes obtained with the framework of …