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Getting started wit...
Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
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- Dahlin, Johan, 1986- (författare)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik,Uppsala Univ, Sweden
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(creator_code:org_t)
- Alexandria, VA, United States : American Statistical Association, 2019
- 2019
- Engelska.
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Ingår i: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:CN2, s. 1-41
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Abstract
Ämnesord
Stäng
- 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 in the 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.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Bayesian inference
- state-space models
- particle filtering
- particle Markov chain Monte Carlo
- stochastic volatility model
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- ref (ämneskategori)
- art (ämneskategori)
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