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arXiv · 2606.25603

Resampling in conditional SMC algorithms

Axel Finke·Adam M. Johansen·Anthony Lee·Lawrence M. Murray
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Abstract

Conditional sequential Monte Carlo (CSMC) algorithms arise in particle Markov chain Monte Carlo and a number of related settings. As in standard sequential Monte Carlo (SMC) algorithms, it is possible to employ a number of approaches to resampling within CSMC, but some additional care is required to arrive at a valid algorithm. We present a simple framework for implementing valid SMC and CSMC algorithms which (a) covers most known resampling schemes including those with a complicated dependence structure like systematic resampling, but also adaptive resampling, and even more `exotic' schemes like a version of chopthin resampling; (b) explains how to implement conditional analogues of these and other well known resampling schemes without randomly permuting/shifting the order of the ancestor indices; (c) requires only very weak assumptions which include neither (marginal) "unbiasedness" nor exchangeability.

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Axel Finke, Adam M. Johansen, Anthony Lee, Lawrence M. Murray. 2026-06-24. Resampling in conditional SMC algorithms. https://arxiv.org/abs/2606.25603

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