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Main Authors: González, Miguel, del Puerto, Inés M., Serrano-Pastor, Manuel
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2605.20987
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author González, Miguel
del Puerto, Inés M.
Serrano-Pastor, Manuel
author_facet González, Miguel
del Puerto, Inés M.
Serrano-Pastor, Manuel
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2605_20987
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Particle filtering methods for partially observed branching processes
González, Miguel
del Puerto, Inés M.
Serrano-Pastor, Manuel
Computation
Probability
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.
title Particle filtering methods for partially observed branching processes
topic Computation
Probability
url https://arxiv.org/abs/2605.20987