A1 Journal article (refereed)
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R (2021)
Helske, J., & Vihola, M. (2021). bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. The R Journal, 13(2), 578-589. https://doi.org/10.32614/RJ-2021-103
JYU authors or editors
Publication details
All authors or editors: Helske, Jouni; Vihola, Matti
Journal or series: The R Journal
eISSN: 2073-4859
Publication year: 2021
Volume: 13
Issue number: 2
Pages range: 578-589
Publisher: R Foundation for Statistical Computing
Publication country: Austria
Publication language: English
DOI: https://doi.org/10.32614/RJ-2021-103
Persistent website address: https://journal.r-project.org/archive/2021/RJ-2021-103/index.html
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/79809
Publication is parallel published: https://arxiv.org/abs/2101.08492
Abstract
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package accommodates also discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package implements also a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers an easy-to-use interface to define models with linear-Gaussian state dynamics with non-Gaussian observation models, and has an Rcpp interface for specifying custom non-linear and diffusion models.
Keywords: mathematics; modelling (representation); mathematical models; Markov chains; Monte Carlo methods; Bayesian analysis
Free keywords: tila-avaruusmallit
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Ministry reporting: Yes
Reporting Year: 2021
JUFO rating: 1