Online Updating of Conditional Linear Gaussian Bayesian Networks

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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.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Probabilistic Graphical Models : PMLR
EditorsAntonio Salmeròn, Rafael Rumi
Number of pages12
PublisherPMLR Press
Publication date2022
Publication statusPublished - 2022
EventInternational Conference on Probabilistic Graphical Models - Almería, Spain
Duration: 5 Oct 20227 Oct 2022


ConferenceInternational Conference on Probabilistic Graphical Models
SeriesThe Proceedings of Machine Learning Research


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