Teaching Probabilistic Graphical Models with OpenMarkov

Francisco Javier Díez (), Manuel Arias, Jorge Pérez-Martín and Manuel Luque
Additional contact information
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

Mathematics, 2022, vol. 10, issue 19, 1-20

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.

Keywords: OpenMarkov; Bayesian Networks; d-separation; inference; Learning Bayesian Networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3577/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3577/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:19:p:3577-:d:930532

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().