Particle filtering methods for partially observed branching processes
Computation
2026-05-21 v1 Probability
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.
Cite
@article{arxiv.2605.20987,
title = {Particle filtering methods for partially observed branching processes},
author = {Miguel González and Inés M. del Puerto and Manuel Serrano-Pastor},
journal= {arXiv preprint arXiv:2605.20987},
year = {2026}
}