Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

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 o...

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Bibliographic Details
Main Authors: Johan Dahlin, Thomas B. Schön
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2019-03-01
Series:Journal of Statistical Software
Subjects:
bayesian inference
state-space models
particle filtering
particle markov chain monte carlo
stochastic volatility model
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/2469

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https://www.jstatsoft.org/index.php/jss/article/view/2469

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