3 papers
stat.CO2026
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…
stat.CO2026
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…
stat.CO2025
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…