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 editorsHelske, Jouni; Vihola, Matti

Journal or seriesThe R Journal

eISSN2073-4859

Publication year2021

Volume13

Issue number2

Pages range578-589

PublisherR Foundation for Statistical Computing

Publication countryAustria

Publication languageEnglish

DOIhttps://doi.org/10.32614/RJ-2021-103

Persistent website addresshttps://journal.r-project.org/archive/2021/RJ-2021-103/index.html

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/79809

Publication is parallel publishedhttps://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.


Keywordsmathematicsmodelling (representation)mathematical modelsMarkov chainsMonte Carlo methodsBayesian analysis

Free keywordstila-avaruusmallit


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Ministry reportingYes

Reporting Year2021

JUFO rating1


Last updated on 2024-26-03 at 09:20