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A review of inference algorithms for hybrid Bayesian networks

Authors:

Antonio Salmerón,

Helge Langseth,

Thomas D. Nielsen,

Anders L. MadsenAuthors Info & Claims

Journal of Artificial Intelligence Research, Volume 62, Issue 1

Pages 799 - 828

https://doi.org/10.1613/jair.1.11228

Published: 01 May 2018 Publication History

Abstract

Hybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which extends the applicability of the Bayesian network framework in general. However, this extra feature also comes at a cost: inference in these types of models is computationally more challenging and the underlying models and updating procedures may not even support closed-form solutions. In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks. The methods covered in the paper are organized and discussed according to their methodological basis. We consider how the methods have been extended and adapted to also include (hybrid) dynamic Bayesian networks, and we end with an overview of established software systems supporting inference in these types of models.

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Information & Contributors

Information

Published In

cover image Journal of Artificial Intelligence Research

Journal of Artificial Intelligence Research Volume 62, Issue 1

May 2018

871 pages

ISSN:1076-9757

Editor:
Rossi Francesca
Department of Mathematics, Padova, Italy

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AI Access Foundation

El Segundo, CA, United States

Publication History

Published: 01 May 2018

Published in JAIR Volume 62, Issue 1

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Cited By

Li JPonnada AWang WDunton GIntille S(2024)Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information GainProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997358:4(1-32)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699735
Krapu CStewart RRose A(2023)A Review of Bayesian Networks for Spatial DataACM Transactions on Spatial Algorithms and Systems10.1145/35165239:1(1-21)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3516523
Vonk MMalekovic NBäck TKononova A(2023)Disentangling causality: assumptions in causal discovery and inferenceArtificial Intelligence Review10.1007/s10462-023-10411-956:9(10613-10649)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1007/s10462-023-10411-9
Asvija BEswari RBijoy M(2020)Bayesian attack graphs for platform virtualized infrastructures in cloudsJournal of Information Security and Applications10.1016/j.jisa.2020.10245551:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.jisa.2020.102455
Borghoff UMatthews SPrüßing HSchäfer CStuke O(2019)A Latent Variable Model in Conflict ResearchComputer Aided Systems Theory – EUROCAST 201910.1007/978-3-030-45093-9_5(36-43)Online publication date: 17-Feb-2019
https://dl.acm.org/doi/10.1007/978-3-030-45093-9_5
Ziebarth JCui Y(2019)Multiscalar genetic pathway modeling with hybrid Bayesian networksWIREs Computational Statistics10.1002/wics.147912:1Online publication date: 13-Dec-2019
https://dl.acm.org/doi/10.1002/wics.1479

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