Particle filtering methods for partially observed branching processes

TL;DR

This paper introduces sequential Monte Carlo methods, specifically the Liu-West particle filter, for Bayesian inference in partially observed branching processes, with applications to epidemic modeling.

Contribution

It presents computational tools based on particle filtering for Bayesian estimation in partially observed branching processes, extending previous approaches.

Findings

Liu-West particle filter effectively estimates parameters in epidemic models

Bayesian inference improves understanding of partially observed processes

Application to epidemic data demonstrates practical utility

Abstract

This paper focuses on the estimation of partially observed branching processes. First, the estimators from a frequentist perspective proposed in the literature are reviewed. The main objective of this paper is to present computational tools based on sequential Monte Carlo methods to perform Bayesian inference for these processes. In particular, the Liu-West particle filter is applied to perform Bayesian estimation of the parameters of interest for an epidemic model fitted by a partially observed branching process. As application, the example given in [8] is revisited and extended.