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Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9424-1272
Uppsala Univ, Sweden.
2019 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, no CN2Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Alexandria, VA, United States: American Statistical Association , 2019. Vol. 88, no CN2
Keywords [en]
Bayesian inference; state-space models; particle filtering; particle Markov chain Monte Carlo; stochastic volatility model
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-156400DOI: 10.18637/jss.v088.c02ISI: 000463413300001OAI: oai:DiVA.org:liu-156400DiVA, id: diva2:1305708
Note

Funding Agencies|project: Probabilistic modeling of dynamical systems, CADICS, a Linnaeus Center - Swedish Research Council [621-2013-5524]; project: Probabilistic modeling of dynamical systems, ASSEMBLE - Swedish Foundation for Strategic Research (SSF) [RIT15-0012]

Available from: 2019-04-18 Created: 2019-04-18 Last updated: 2019-10-22Bibliographically approved

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Dahlin, Johan

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Citation style
  • apa
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