9 citations · 21 across the 9 of their papers we have counts for
9 papers
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…
Continuously-Tempered PDMP Samplers
Matthew Sutton, Robert Salomone, Augustin Chevallier +1
New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However,…
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…
PDMP Monte Carlo methods for piecewise-smooth densities
Augustin Chevallier, Sam Power, Andi Q. Wang +1
There has been substantial interest in developing Markov chain Monte Carlo algorithms based on piecewise-deterministic Markov processes. However existing algorithms can only be use…
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…
NuZZ: numerical Zig-Zag sampling for general models
Filippo Pagani, Augustin Chevallier, Sam Power +2
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 become too co…
Pathwise error bounds in Multiscale variable splitting methods for spatial stochastic kinetics
Augustin Chevallier, Stefan Engblom
Stochastic computational models in the form of pure jump processes occur frequently in the description of chemical reactive processes, of ion channel dynamics, and of the spread of…