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An Elementary Introduction to Sequential Monte Carlo Samplers

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If you have a question about this talk, please contact Xianda Sun .

Sequential Monte Carlo (SMC) methods provide a compelling alternative to traditional Markov Chain Monte Carlo (MCMC) approaches for sampling from unnormalized probability distributions while also delivering unbiased estimates of the normalizing constant. Unlike MCMC , which generates a single chain of samples, SMC methods operate by evolving a population of weighted particles through a sequence of intermediate distributions, ultimately converging on the target density.

Despite their flexibility and strong theoretical underpinnings, SMC samplers are still underutilized in practice. This talk offers a conceptual introduction to SMC samplers, following the framework outlined by Dai, Heng, Jacob, and Whiteley in their paper ”An Invitation to Sequential Monte Carlo Samplers.” Instead of focusing on state-space models, the presentation will unpack the essential components of SMC samplers, making them more accessible to beginners and researchers in probabilistic machine learning and related disciplines. The goal is to provide a stepping stone into the broader, rich literature on SMC methods.

This talk is part of the Machine Learning Reading Group @ CUED series.

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