Miguel Palacios-Alonso

Bio: Miguel Palacios-Alonso is an academic researcher. The author has contributed to research in topics: Graphical model & Markov process. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

ProbModelXML. A format for encoding probabilistic graphical models

Abstract: ProbModelXML is an XML format for encoding probabilistic graphical models. The main advan- tages of this format are that it can represent several kinds of models, such as Bayesian networks, Markov networks, influence diagrams, LIMIDs, decision analysis networks, as well as tempo- ral models: dynamic Bayesian networks, MDPs, POMDPs, Markov processes with atemporal decisions (MPADs), DLIMIDs, etc., and the possibility of encoding new types of networks and user-specific properties without the need to modify the format definition.

Clinical evidence framework for Bayesian networks

Abstract: There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.

Interactive learning of Bayesian Networks using OpenMarkov

Abstract: Algorithms for learning Bayesian networks (BNs) behave as a black box that takes a database as an input and returns a network as the output. In contrast, OpenMarkov, our tool for probabilistic graphical models, includes the option to run the algorithms in a step-by-step fashion, presenting a ranked list of operations (such as adding, removing, or inverting links) the user can select, while allowing live edition of the BN throughout the learning process. The application oers some data preprocessing options and the possibility to use a model network to guide the learning process. This functionality in OpenMarkov can be employed to learn BNs with partial expert knowledge, to debug new algorithms, and as a pedagogical tool.

OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models.

Abstract: 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.