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.

翻译：本文聚焦于部分观测分支过程的估计问题。首先，回顾了文献中从频率学派角度提出的估计量。本文的主要目标是介绍基于序贯蒙特卡洛方法的计算工具，以实现对这些过程的贝叶斯推断。特别地，应用Liu-West粒子滤波器对由部分观测分支过程拟合的流行病模型中的感兴趣参数进行贝叶斯估计。作为应用，重新审视并拓展了文献[8]中的示例。