Automatic Regenerative Simulation via Non-Reversible Simulated Tempering

• 成果类型: Article
• 署名作者: Biron-Lattes, Miguel; Campbell, Trevor; Bouchard-Cote, Alexandre
• 署名单位: University of British Columbia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2335587
• 发表日期: 2025
• 页码: 318-330
• 关键词: chain monte-carlo; parallel

摘要

Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and a more amenable reference distribution. Crucially, if the reference enables iid sampling, ST is regenerative and can be parallelized across independent tours. However, the difficulty of tuning ST has hindered its widespread adoption. In this work, we develop a simple nonreversible ST (NRST) algorithm, a general theoretical analysis of ST, and an automated tuning procedure for ST. A core contribution that arises from the analysis is a novel performance metric-Tour Effectiveness (TE)-that controls the asymptotic variance of estimates from ST for bounded test functions. We use the TE to show that NRST dominates its reversible counterpart. We then develop an automated tuning procedure for NRST algorithms that targets the TE while minimizing computational cost. This procedure enables straightforward integration of NRST into existing probabilistic programming languages. We provide extensive experimental evidence that our tuning scheme improves the performance and robustness of NRST algorithms on a diverse set of probabilistic models. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.