Persona:
Díez Vegas, Francisco Javier

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0000-0001-9855-9248
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Díez Vegas
Nombre de pila
Francisco Javier
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2021 - 20222
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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Sum-Product Networks: A Survey
    (IEEE, 2021-02-25) Sánchez Cauce, Raquel; París Fernández, Iago; Díez Vegas, Francisco Javier
    A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.
  • Miniatura
    Publicación
    Teaching Probabilistic Graphical Models with OpenMarkov
    (MDPI, 2022-11-30) Díez Vegas, Francisco Javier; Arias Calleja, Manuel; Pérez Martín, Jorge; Luque Gallego, Manuel
    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.