Online Updating of Conditional Linear Gaussian Bayesian Networks

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Abstract

This paper presents a method for online updating of conditional distributions in Bayesian network models with both discrete and continuous variables. The method extends known procedures for updating discrete conditional probability distributions with techniques to cope with conditional Gaussian density functions. The method has a solid foundation for known cases and may be generalised by a heuristic scheme for fractional updating when discrete parents are not known. A fading mechanism is described to prevent the system being too conservative as cases accumulate over long time periods. The effect of the online updating is illustrated by an application to predict the number of waiting patients at the emergency department at Aalborg University Hospital.
OriginalsprogEngelsk
TitelProceedings of the 11th International Conference on Probabilistic Graphical Models : PMLR
RedaktørerAntonio Salmeròn, Rafael Rumi
Antal sider12
Vol/bind186
ForlagPMLR Press
Publikationsdato2022
Sider97-108
StatusUdgivet - 2022
BegivenhedInternational Conference on Probabilistic Graphical Models - Almería, Spanien
Varighed: 5 okt. 20227 okt. 2022

Konference

KonferenceInternational Conference on Probabilistic Graphical Models
Land/OmrådeSpanien
ByAlmería
Periode05/10/202207/10/2022
NavnThe Proceedings of Machine Learning Research
Vol/bind186
ISSN2640-3498

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