Adaptive schemes for piecewise deterministic Monte Carlo algorithms

Andrea Bertazzi, Joris Bierkens

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
37 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)2404-2430
Number of pages27
JournalBernoulli
Volume28
Issue number4
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Adaptive Markov process Monte Carlo
  • bouncy particle sampler
  • ergodicity
  • piecewise deterministic Markov processes
  • zig-zag sampler

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