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Coordinate sampler: a non-reversible Gibbs-like MCMC sampler

Wu, Changye; Robert, Christian P. (2020), Coordinate sampler: a non-reversible Gibbs-like MCMC sampler, Statistics and Computing, 30, p. 721–730. 10.1007/s11222-019-09913-w

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Type
Article accepté pour publication ou publié
Date
2020
Journal name
Statistics and Computing
Volume
30
Publisher
Springer
Published in
Paris
Pages
721–730
Publication identifier
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Author(s)
Wu, Changye
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Robert, Christian P.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
We derive a novel non-reversible, continuous-time Markov chain Monte Carlo sampler, called Coordinate Sampler, based on a piecewise deterministic Markov process, which is a variant of the Zigzag sampler of Bierkens et al. (Ann Stat 47(3):1288–1320, 2019). In addition to providing a theoretical validation for this new simulation algorithm, we show that the Markov chain it induces exhibits geometrical ergodicity convergence, for distributions whose tails decay at least as fast as an exponential distribution and at most as fast as a Gaussian distribution. Several numerical examples highlight that our coordinate sampler is more efficient than the Zigzag sampler, in terms of effective sample size.
Subjects / Keywords
Markov chain Monte Carlo; Piecewise deterministic Markovprocesses; Zigzag sampling; Gibbs sampling