A Review of Inference Algorithms for Hybrid Bayesian Networks

Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas Dyhre Nielsen, Anders Læsø Madsen

Research output: Contribution to journalReview articleResearchpeer-review

4 Citations (Scopus)
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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.
Original languageEnglish
JournalJournal of Artificial Intelligence Research
Pages (from-to)799-828
Number of pages30
Publication statusPublished - 1 Aug 2018


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