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Teaching Probabilistic Graphical Models with OpenMarkov

Author

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  • Francisco Javier Díez

    (Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain)

  • Manuel Arias

    (Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain)

  • Jorge Pérez-Martín

    (Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain)

  • Manuel Luque

    (Department of Statistics, Operations Research and Numerical Calculation, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain)

Abstract

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.

Suggested Citation

  • Francisco Javier Díez & Manuel Arias & Jorge Pérez-Martín & Manuel Luque, 2022. "Teaching Probabilistic Graphical Models with OpenMarkov," Mathematics, MDPI, vol. 10(19), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3577-:d:930532
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    References listed on IDEAS

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    1. Steffen L. Lauritzen & Dennis Nilsson, 2001. "Representing and Solving Decision Problems with Limited Information," Management Science, INFORMS, vol. 47(9), pages 1235-1251, September.
    2. James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
    3. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.

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