Approximate filtering via discrete dual processes

TL;DR

This paper develops a framework for approximate filtering of diffusion processes using dual processes on discrete state spaces, enabling efficient recursive algorithms and comparisons with existing methods.

Contribution

It extends duality-based filtering methods to jump Markov chains, providing new approximation strategies and performance analysis for specific diffusion models.

Findings

Duality leads to finite mixture filtering distributions.

Approximation strategies perform well on Cox-Ingersoll-Ross and Wright-Fisher models.

Compared methods show advantages over bootstrap particle filtering.

Abstract

We consider the task of filtering a dynamic parameter evolving as a diffusion process, given data collected at discrete times from a likelihood which is conjugate to the marginal law of the diffusion, when a generic dual process on a discrete state space is available. Recently, it was shown that duality with respect to a death-like process implies that the filtering distributions are finite mixtures, making exact filtering and smoothing feasible through recursive algorithms with polynomial complexity in the number of observations. Here we provide general results for the case of duality between the diffusion and a regular jump continuous-time Markov chain on a discrete state space, which typically leads to filtering distribution given by countable mixtures indexed by the dual process state space. We investigate the performance of several approximation strategies on two hidden Markov models driven by Cox-Ingersoll-Ross and Wright-Fisher diffusions, which admit duals of birth-and-death type, and compare them with the available exact strategies based on death-type duals and with bootstrap particle filtering on the diffusion state space as a general benchmark.