5 citations · 6 across the 5 of their papers we have counts for
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Establishing an complexity lower bound for PDMP samplers and how to break it: a sub- algorithm for Gaussian-tailed targets
Augustin Chevallier
Despite the theoretical appeal of their non-reversibility, to date, no Piecewise Deterministic Markov Process (PDMP) samplers have been developed that scale better than $\mathcal{O…
Covariance-Adaptive Bouncy Particle Samplers via Split Lagrangian Dynamics
Augustin Chevallier, Erik Raab
Piecewise Deterministic Markov Processes (PDMPs) provide a powerful framework for continuous-time Monte Carlo, with the Bouncy Particle Sampler (BPS) as a prominent example. Recent…
Towards practical PDMP sampling: Metropolis adjustments, locally adaptive step-sizes, and NUTS-based time lengths
Augustin Chevallier, Sam Power, Matthew Sutton
Piecewise-Deterministic Markov Processes (PDMPs) hold significant promise for sampling from complex probability distributions. However, their practical implementation is hindered b…
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
Augustin Chevallier, Frédéric Cazals, Paul Fearnhead
Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications. Current state-of-the-art algorithms to compute such volumes rely on…
Reversible Jump PDMP Samplers for Variable Selection
Augustin Chevallier, Paul Fearnhead, Matthew Sutton
A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-rev…