%0 Generic %1 dahlin2015getting %A Dahlin, Johan %A Schön, Thomas B. %D 2015 %K bayesian online tutorial %T Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models %U http://arxiv.org/abs/1511.01707 %X We provide a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state space models (SSMs) together with a software implementation in the statistical programming language R. Throughout this tutorial, we develop an implementation of the PMH algorithm (and the integrated particle filter), which is distributed as the package pmhtutorial available from the CRAN repository. Moreover, we provide the reader with some intuition for how the algorithm operates and discuss some solutions to numerical problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian SSM with synthetic data and a nonlinear stochastic volatility model with real-world data. We conclude the tutorial by discussing important possible improvements to the algorithm and we also list suitable references for further study.

@misc{dahlin2015getting, abstract = {We provide a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state space models (SSMs) together with a software implementation in the statistical programming language R. Throughout this tutorial, we develop an implementation of the PMH algorithm (and the integrated particle filter), which is distributed as the package pmhtutorial available from the CRAN repository. Moreover, we provide the reader with some intuition for how the algorithm operates and discuss some solutions to numerical problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian SSM with synthetic data and a nonlinear stochastic volatility model with real-world data. We conclude the tutorial by discussing important possible improvements to the algorithm and we also list suitable references for further study.}, added-at = {2016-04-01T07:00:50.000+0200}, author = {Dahlin, Johan and Schön, Thomas B.}, biburl = {https://www.bibsonomy.org/bibtex/2d90a16b74b9f2308e71c72577ac4ad5d/pixor}, description = {1511.01707v4.pdf}, interhash = {a8a33049fb6d0b399d7206c58d5f5420}, intrahash = {d90a16b74b9f2308e71c72577ac4ad5d}, keywords = {bayesian online tutorial}, note = {cite arxiv:1511.01707v4.pdfComment: 36 pages, 8 figures. Submitted to Journal of Statisical Software. Fixed typos and made minior revisions. Source code for R, Python and MATLAB available at: https://github.com/compops/pmh-tutorial}, timestamp = {2016-04-01T07:00:50.000+0200}, title = {Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models}, url = {http://arxiv.org/abs/1511.01707}, year = 2015 }