Central Limit Theorems for Coupled Particle Filters

TL;DR

This paper establishes a new central limit theorem for coupled particle filters, introduces a maximal coupling approximation, and demonstrates improved variance bounds in multilevel Monte Carlo methods for diffusion processes.

Contribution

It develops a new CLT for CPFs, proposes a maximal coupling CPF, and shows improved asymptotic variance bounds in MLMC estimations for discretized diffusions.

Findings

New CLT for coupled particle filters.

Introduction of a maximal coupling CPF (MCPF).

Asymptotic variance bounds are nearly proportional to discretization step size.

Abstract

In this article we prove a new central limit theorem (CLT) for coupled particle filters (CPFs). CPFs are used for the sequential estimation of the difference of expectations w.r.t. filters which are in some sense close. Examples include the estimation of the filtering distribution associated to different parameters (finite difference estimation) and filters associated to partially observed discretized diffusion processes (PODDP) and the implementation of the multilevel Monte Carlo (MLMC) identity. We develop new theory for CPFs and based upon several results, we propose a new CPF which approximates the maximal coupling (MCPF) of a pair of predictor distributions. In the context of ML estimation associated to PODDP with discretization $\Delta_l$ we show that the MCPF and the approach in Jasra et al. (2018) have, under assumptions, an asymptotic variance that is upper-bounded by an expression that is (almost) $\mathcal{O}(\Delta_l)$, uniformly in time. The $\mathcal{O}(\Delta_l)$ rate preserves the so-called forward rate of the diffusion in some scenarios which is not the case for the CPF in Jasra et al (2017).