Adaptive schemes for piecewise deterministic Monte Carlo algorithms
From MaRDI portal
Publication:2676925
DOI10.3150/21-BEJ1423zbMATH Open1501.65001arXiv2012.13924OpenAlexW3114466450MaRDI QIDQ2676925
Author name not available (Why is that?)
Publication date: 28 September 2022
Published in: (Search for Journal in Brave)
Abstract: The Bouncy Particle sampler (BPS) and the Zig-Zag sampler (ZZS) are continuous time, non-reversible Monte Carlo methods based on piecewise deterministic Markov processes. Experiments show that the speed of convergence of these samplers can be affected by the shape of the target distribution, as for instance in the case of anisotropic targets. We propose an adaptive scheme that iteratively learns all or part of the covariance matrix of the target and takes advantage of the obtained information to modify the underlying process with the aim of increasing the speed of convergence. Moreover, we define an adaptive scheme that automatically tunes the refreshment rate of the BPS or ZZS. We prove ergodicity and a law of large numbers for all the proposed adaptive algorithms. Finally, we show the benefits of the adaptive samplers with several numerical simulations.
Full work available at URL: https://arxiv.org/abs/2012.13924
No records found.
No records found.
This page was built for publication: Adaptive schemes for piecewise deterministic Monte Carlo algorithms
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q2676925)
