Approximate filtering via discrete dual processes

Guillaume Kon Kam King, Andrea Pandolfi, Marco Piretto and Matteo Ruggiero

Stochastic Processes and their Applications, 2024, vol. 168, issue C

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 reversible 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 where the dual is 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.

Keywords: Bayesian inference; Diffusion; Duality; Hidden Markov models; Particle filtering; Smoothing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spa.2023.104268

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