Sequential Bayesian inference for spatio-temporal models of temperature and humidity data

•The paper develops a novel spatio-temporal model for temperature and humidity data.•The model is fit to streaming data by using a cutting-edge sequential Monte Carlo algorithm: the iterated batch importance sampling scheme.•We derive an on-line implementation of this algorithm which is more efficie...

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Vydané v:	Journal of computational science Ročník 43; s. 101125
Hlavní autori:	Lai, Yingying, Golightly, Andrew, Boys, Richard J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.05.2020
Predmet:	Dynamic linear models (DLMs) Iterated batch importance sampling (IBIS) Parallel computing Sequential Monte Carlo (SMC) Dynamic linear models (DLMs) Parallel computing Iterated batch importance sampling (IBIS) Sequential Monte Carlo (SMC)
ISSN:	1877-7503, 1877-7511
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