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Teaching Probabilistic Graphical Models with OpenMarkov

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dc.contributor.authorDíez Vegas, Francisco Javier
dc.contributor.authorArias Calleja, Manuel
dc.contributor.authorPérez Martín, Jorge
dc.contributor.authorLuque Gallego, Manuel
dc.date.accessioned2024-05-20T11:43:11Z
dc.date.available2024-05-20T11:43:11Z
dc.date.issued2022-11-30
dc.description.abstractOpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC.en
dc.description.versionversión publicada
dc.identifier.doi10.3390/math10193577
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12463
dc.journal.issue9
dc.journal.titleMathematics
dc.journal.volume10
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordsOpenMarkov
dc.subject.keywordsBayesian Networks
dc.subject.keywordsd-separation
dc.subject.keywordsinference
dc.subject.keywordsLearning Bayesian Networks
dc.titleTeaching Probabilistic Graphical Models with OpenMarkoves
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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