REGENERATION-ENRICHED MARKOV PROCESSES WITH APPLICATION TO MONTE CARLO

• 成果类型: Article
• 署名作者: Wang, Andi Q.; Pollock, Murray; Roberts, Gareth O.; Steinsaltz, David
• 署名单位: University of Bristol; Newcastle University - UK; University of Warwick; University of Oxford
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/20-AAP1602
• 发表日期: 2021
• 页码: 703-735
• 关键词: total variation approximation; distributions; simulation

摘要

We study a class of Markov processes that combine local dynamics, arising from a fixed Markov process, with regenerations arising at a state-dependent rate. We give conditions under which such processes possess a given target distribution as their invariant measures, thus making them amenable for use within Monte Carlo methodologies. Since the regeneration mechanism can compensate the choice of local dynamics, while retaining the same invariant distribution, great flexibility can be achieved in selecting local dynamics, and the mathematical analysis is simplified. We give straightforward conditions for the process to possess a central limit theorem, and additional conditions for uniform ergodicity and for a coupling from the past construction to hold, enabling exact sampling from the invariant distribution. We further consider and analyse a natural approximation of the process which may arise in the practical simulation of some classes of continuous-time dynamics.