文章基本信息

标题：Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
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作者：Johan Dahlin ; Thomas B. Schön
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：88
期号：1
页码：1-41
DOI：10.18637/jss.v088.c02
出版社：University of California, Los Angeles
摘要：This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial from the Comprehensive R Archive Network (CRAN) repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.
关键词：Bayesian inference; state-space models; particle filtering; particle Markov chain Monte Carlo; stochastic volatility model
其他关键词：Bayesian inference;state-space models;particle filtering;particle Markov chain Monte Carlo;stochastic volatility model