Sequential Monte Carlo as Approximate Sampling: bounds, adaptive resampling via $\infty$-ESS, and an application to Particle Gibbs

TL;DR

This paper introduces an adaptive resampling method for Sequential Monte Carlo algorithms that controls divergence from the target distribution using a novel effective sample size measure, improving sampling efficiency and providing theoretical guarantees.

Contribution

It proposes the $ abla$-ESS, a new measure for adaptive resampling in SMC, and develops divergence bounds and ergodicity guarantees for adaptive Particle Gibbs algorithms.

Findings

The $ abla$-ESS effectively controls divergence in SMC.

Adaptive resampling improves efficiency over fixed schemes.

Theoretical guarantees match those of nonadaptive algorithms.

Abstract

Sequential Monte Carlo (SMC) algorithms were originally designed for estimating intractable conditional expectations within state-space models, but are now routinely used to generate approximate samples in the context of general-purpose Bayesian inference. In particular, SMC algorithms are often used as subroutines within larger Monte Carlo schemes, and in this context, the demands placed on SMC are different: control of mean-squared error is insufficient---one needs to control the divergence from the target distribution directly. Towards this goal, we introduce the conditional adaptive resampling particle filter, building on the work of Gordon, Salmond, and Smith (1993), Andrieu, Doucet, and Holenstein (2010), and Whiteley, Lee, and Heine (2016). By controlling a novel notion of effective sample size, the $\infty$-ESS, we establish the efficiency of the resulting SMC sampling algorithm, providing an adaptive resampling extension of the work of Andrieu, Lee, and Vihola (2013). We apply our results to arrive at new divergence bounds for SMC samplers with adaptive resampling as well as an adaptive resampling version of the Particle Gibbs algorithm with the same geometric-ergodicity guarantees as its nonadaptive counterpart.