Bayesian inference for partially observed branching processes: particle filtering methods

M. González Velasco, I. del Puerto García

In this work we deal with the estimation of the main parameters of partially observed branching processes from a Bayesian perspective. We use particle filtering methodologies, as the Liu and West's filter and particle learning. We show the accuracy of the proposed methodology via simulated examples motivated by epidemiological applications and making use of the statistical software R.

Acknowledgements:
This research has been supported by grant PID2023-152359NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF/EU.

Keywords: branching processes; Bayesian methods; Particle filters

Stochastic processes and their applications IV
June 13, 2025  9:00 AM
Auditorio 2. Leandre Cristòfol

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