Persona:
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 - 20194
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Fecha de finalización: 2019 × Fecha de inicio: 2010 ×

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Mostrando 1 - 4 de 4
  • Miniatura
    Publicación
    OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models
    (International Joint Conference on Artificial Intelligence, 2019) Arias Calleja, Manuel; Pérez Martín, Jorge; Luque Gallego, Manuel; Díez Vegas, Francisco Javier
    OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models. With more than 100,000 lines of code, it offers some features for interactive learning, explanation of reasoning, and cost-effectiveness analysis, which are not available in any other tool. OpenMarkov has been used at universities, research centers, and large companies in more than 30 countries on four continents. Several models, some of them for real-world medical applications, built with OpenMarkov, are publicly available on Internet.
  • Miniatura
    Publicación
    Automatic assignment of reviewers in an online peer assessment task based on social interactions
    (Wiley Online Library, 2019) Anaya, Antonio R.; Luque Gallego, Manuel; Letón Molina, Emilio; Hernández del Olmo, Félix
    Online peer assessment tasks are very popular and have unique characteristics that improve learning and encourage social interactions in a distance education environment. Unfortunately, social factors have usually been ignored in the process of selecting reviewers for online peer assessment tasks. We hypothesise that this fact could have some influence on the lack of engagement and participation by some learners. For this reason, we propose an approach in which social network analysis techniques, expert criteria, and Bayesian reasoning are applied to select reviewers with the objective of increasing participation in peer review tasks. The approach is divided into two elements. On the one hand, we have developed an influence diagram template that structures a set of proposed social network analysis variables in accordance with expert criteria. This influence diagram template can be easily updated for any course simply by eliciting a minimal set of parameters. On the other hand, we have instantiated the proposed influence diagram template to produce an influence diagram network to quantify the quality of reviewer assignment for an online peer assessment task. In an online experiment, we verified that the consideration of social factors can increase participation in a peer assessment task.
  • 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.