Irregular-Time Bayesian Networks

Michael Ramati, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic differential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of the available data.
Original languageEnglish
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Place of PublicationCatalina Island, CA, USA
PublisherAUAI Press
Pages484-491
Number of pages8
ISBN (Print)978-0-9749039-6-5.
StatePublished - 2010

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