Teaching Probabilistic Graphical Models with OpenMarkov
OpenMarkov 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/19/3577 |
| _version_ | 1797478151985561600 |
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| author | Francisco Javier Díez Manuel Arias Jorge Pérez-Martín Manuel Luque |
| author_facet | Francisco Javier Díez Manuel Arias Jorge Pérez-Martín Manuel Luque |
| author_sort | Francisco Javier Díez |
| collection | DOAJ |
| description | OpenMarkov 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. |
| first_indexed | 2024-03-09T21:27:54Z |
| format | Article |
| id | doaj.art-c30418bf8fc546b185ffbde87c8e8594 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T21:27:54Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c30418bf8fc546b185ffbde87c8e85942023-11-23T21:03:54ZengMDPI AGMathematics2227-73902022-09-011019357710.3390/math10193577Teaching Probabilistic Graphical Models with OpenMarkovFrancisco Javier Díez0Manuel Arias1Jorge Pérez-Martín2Manuel Luque3Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainDepartment of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainDepartment of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainDepartment of Statistics, Operations Research and Numerical Calculation, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainOpenMarkov 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.https://www.mdpi.com/2227-7390/10/19/3577OpenMarkovBayesian Networksd-separationinferenceLearning Bayesian Networks |
| spellingShingle | Francisco Javier Díez Manuel Arias Jorge Pérez-Martín Manuel Luque Teaching Probabilistic Graphical Models with OpenMarkov Mathematics OpenMarkov Bayesian Networks d-separation inference Learning Bayesian Networks |
| title | Teaching Probabilistic Graphical Models with OpenMarkov |
| title_full | Teaching Probabilistic Graphical Models with OpenMarkov |
| title_fullStr | Teaching Probabilistic Graphical Models with OpenMarkov |
| title_full_unstemmed | Teaching Probabilistic Graphical Models with OpenMarkov |
| title_short | Teaching Probabilistic Graphical Models with OpenMarkov |
| title_sort | teaching probabilistic graphical models with openmarkov |
| topic | OpenMarkov Bayesian Networks d-separation inference Learning Bayesian Networks |
| url | https://www.mdpi.com/2227-7390/10/19/3577 |
| work_keys_str_mv | AT franciscojavierdiez teachingprobabilisticgraphicalmodelswithopenmarkov AT manuelarias teachingprobabilisticgraphicalmodelswithopenmarkov AT jorgeperezmartin teachingprobabilisticgraphicalmodelswithopenmarkov AT manuelluque teachingprobabilisticgraphicalmodelswithopenmarkov |