Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition

We prove bounds on the variance of a function $f$ under the empirical measure of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with time complexity depending on local rather than global Markov chain mixing dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts...

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Bibliographic Details
Main Authors Lee, Holden, Santana-Gijzen, Matheau
Format Journal Article
LanguageEnglish
Published 29.05.2024
Subjects
Computer Science - Learning
Mathematics - Probability
Mathematics - Statistics Theory
Statistics - Machine Learning
Statistics - Theory
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Summary:We prove bounds on the variance of a function $f$ under the empirical measure of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with time complexity depending on local rather than global Markov chain mixing dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts by drawing $N$ particles from a known distribution, and then, through a sequence of distributions, re-weights and re-samples the particles, at each instance applying a Markov chain for smoothing. In principle, SMC tries to alleviate problems from multi-modality. However, most theoretical guarantees for SMC are obtained by assuming global mixing time bounds, which are only efficient in the uni-modal setting. We show that bounds can be obtained in the truly multi-modal setting, with mixing times that depend only on local MCMC dynamics.
DOI:10.48550/arxiv.2405.19553