Making continuous time Bayesian networks more flexible

Manxia Liu, Fabio Stella, Arjen Hommersom, Peter J.F. Lucas

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

4 Citations (Scopus)
17 Downloads (Pure)

Abstract

The time duration in continuous time Bayesian networks, i.e., the time that a variable stays in a state until it transitions to another state, follows an exponential distribution. The exponential distribution is widely applied to describe the waiting time between events in a Poisson process, which describes the distribution of the number of events in one unit of time. This distribution is parameterized by a single rate and has mode zero, implying that the highest probability mass for events to happen is attributed to the earliest times. To describe biological processes, the exponential distribution is not always natural. For example, if the immune system has not encountered a pathogen before, it most likely responds to a viral infection after a few days, rather than immediately. In this paper, we generalize our recently proposed hypoexponential continuous time Bayesian networks, by allowing any number of hypoexponential variables, i.e., variables having a hypoexponential time duration distribution. In addition, we propose and compare two learning methods to estimate parameters for the generalized models. Finally, the practical value of the generalized models is demonstrated by means of a realistic medical problem.
Original languageEnglish
Title of host publicationProceedings of the Ninth International Conference on Probabilistic Graphical Models
Subtitle of host publication11-14 September 2018, Prague, Czech Republic
EditorsVáclav Kratochvíl, Milan Studený
PublisherMLResearchPress
Pages237-248
Number of pages12
Publication statusPublished - 2018
Externally publishedYes
Event9th International Conference on Probabilistic Graphical Models, PGM 2018 - Prague, Czech Republic
Duration: 11 Sept 201814 Sept 2018
Conference number: 9

Publication series

NameProceedings of Machine Learning Research (PMLR)
PublisherJMLR
Volume72
ISSN (Electronic)2640-3498

Conference

Conference9th International Conference on Probabilistic Graphical Models, PGM 2018
Abbreviated titlePGM 2018
Country/TerritoryCzech Republic
CityPrague
Period11/09/1814/09/18

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