Continuously-Tempered PDMP samplers

Sutton, Matthew and Salomone, Robert and Chevallier, Augustin and Fearnhead, Paul (2022) Continuously-Tempered PDMP samplers. Proceedings of Machine Learning Research. ISSN 1938-7228 (In Press)

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Abstract

New sampling algorithms based on simulating continuous-time stochastic processes called piecewise deterministic Markov processes (PDMPs) have shown considerable promise. However, these methods can struggle to sample from multi-modal or heavy-tailed distributions. We show how tempering ideas can improve the mixing of PDMPs in such cases. We introduce an extended distribution defined over the state of the posterior distribution and an inverse temperature, which interpolates between a tractable distribution when the inverse temperature is 0 and the posterior when the inverse temperature is 1. The marginal distribution of the inverse temperature is a mixture of a continuous distribution on [0,1) and a point mass at 1: which means that we obtain samples when the inverse temperature is 1, and these are draws from the posterior, but sampling algorithms will also explore distributions at lower temperatures which will improve mixing. We show how PDMPs, and particularly the Zig-Zag sampler, can be implemented to sample from such an extended distribution. The resulting algorithm is easy to implement and we show empirically that it can outperform existing PDMP-based samplers on challenging multimodal posteriors.

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of Machine Learning Research
ID Code:
176536
Deposited By:
Deposited On:
23 Sep 2022 15:30
Refereed?:
Yes
Published?:
In Press
Last Modified:
02 Dec 2022 00:55