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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of computational science Jg. 43; S. 101125
Hauptverfasser:	Lai, Yingying, Golightly, Andrew, Boys, Richard J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2020
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!