{"type":"ScholarlyArticle","title":"Probabilistic Inference in Hybrid Domains by Weighted Model Integration","authors":[{"name":"Vaishak Belle","slug":"vaishak-belle","given":"Vaishak","family":"Belle","affiliation":"Dept. of Computer Science KU Leuven , Belgium"},{"name":"Andrea Passerini","slug":"andrea-passerini","given":"Andrea","family":"Passerini","affiliation":"DISI University of Trento , Italy"},{"name":"Guy Van den Broeck","slug":"guy-van-den-broeck","given":"Guy","family":"Van den Broeck","affiliation":"Dept. of Computer Science KU Leuven , Belgium"}],"abstract":"Weighted model counting (WMC) on a propositional knowledge base is an effective and general approach to probabilistic inference in a variety of formalisms, including Bayesian and Markov Networks. However, an inherent limitation of WMC is that it only admits the inference of discrete probability distributions. In this paper, we introduce a strict generalization of WMC called weighted model integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof. This methodology is shown to capture discrete, continuous and hybrid Markov networks. We then consider the task of parameter learning for a fragment of the language. An empirical evaluation demonstrates the applicability and promise of the proposal.","doi":null,"url":"https://ijcai.org/proceedings/2015/392","pdf":"https://ijcai.org/proceedings/2015/0392.pdf","markdown":"https://ijcai.org/proceedings/2015/392.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/392","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Knowledge Representation and Reasoning","track":"Special Track on Knowledge Representation and Reasoning","number":392,"pages":{"first":2770,"last":2775},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Bacchus et al., 2009] F. 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