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Luque Gallego, Manuel

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0000-0003-3018-3760
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Luque Gallego
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Manuel
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2017 - 20182
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Autor: Bermejo, Iñigo × search.filters.applied.f.itemFacultad: E.T.S. de Ingeniería Informática ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Decision analysis networks
    (['Decision analysis', 'Decision trees', 'Influence diagrams', 'Probabilistic graphical models', 'Asymmetric decision problems'], 2018-05) Bermejo, Iñigo; Díez Vegas, Francisco Javier; Luque Gallego, Manuel
    This paper presents decision analysis networks (DANs) as a new type of probabilistic graphical model. Like influence diagrams (IDs), DANs are much more compact and easier to build than decision trees and can represent conditional independencies. In fact, for every ID there is an equivalent symmetric DAN, but DANs can also represent asymmetric problems involving partial orderings of the decisions (order asymmetry), restrictions between the values of the variables (domain asymmetry), and conditional observability (information asymmetry). Symmetric DANs can be evaluated with the same algorithms as IDs. Every asymmetric DAN can be evaluated by converting it into an equivalent decision tree or, much more efficiently, by decomposing it into a tree of symmetric DANs. Given that DANs can solve symmetric problems as easily and as efficiently as IDs, and are more appropriate for asymmetric problems—which include virtually all real-world problems—DANs might replace IDs as the standard type of probabilistic graphical model for decision support and decision analysis. We also argue that DANs compare favorably with other formalisms proposed for asymmetric decision problems. In practice, DANs can be built and evaluated with OpenMarkov, a Java open-source package for probabilistic graphical models.
  • Miniatura
    Publicación
    Markov influence diagrams: a graphical tool for cost-effectiveness analysis
    (Society for Medical Decision Making, 2017-01-11) Yebra, Mar; Bermejo, Iñigo; Palacios Alonso, Miguel Ángel; Arias Calleja, Manuel; Luque Gallego, Manuel; Pérez Martín, Jorge; Díez Vegas, Francisco Javier
    Markov influence diagrams (MIDs) are a new type of probabilistic graphical models that extend influence diagrams in the same way as Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analysis. Using a causal graph that may contain several variables per cycle, MIDs can model various features of the patient without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs, i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.