Open Access
ProbModelXML. A format for encoding probabilistic graphical models
TLDR
XML can represent several kinds of models, such as Bayesian networks, Markov networks, influence diagrams, LIMIDs, decision analysis networks, as well as tempo- ral models, and the possibility of encoding new types of networks and user-specific properties without the need to modify the format definition.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.read more
Citations
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Journal ArticleDOI
Decision analysis networks
TL;DR: It is argued that DANs compare favorably with other formalisms proposed for asymmetric decision problems, and can be built and evaluated with OpenMarkov, a Java open-source package for probabilistic graphical models.
Journal ArticleDOI
Clinical evidence framework for Bayesian networks
TL;DR: A framework for representing the evidence-base of a Bayesian network (BN) decision support model is proposed to be able to present all the clinical evidence alongside the BN itself and allows the completeness of the evidence to be queried.
Interactive learning of Bayesian Networks using OpenMarkov
TL;DR: OpenMarkov, the tool for probabilistic graphical models, includes the option to run algorithms in a step-by-step fashion, presenting a ranked list of operations the user can select, while allowing live edition of the BN throughout the learning process.
Journal ArticleDOI
A context‐awareness model for activity recognition in robot‐assisted scenarios
Francisco Javier Rodríguez Lera,Francisco Martín Rico,Ángel Manuel Guerrero Higueras,Vicente Matellán Olivera +3 more
TL;DR: This research proposes a context‐awareness system for a human–robot scene interpretation based on seven primary contexts and the American Occupational Therapy Association and proposes an inference mechanism for the activity recognition supported on hierarchical Bayesian networks.
Proceedings ArticleDOI
OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models.
TL;DR: OpenMarkov is a Java open-source tool for building and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models, which has been used in universities, research centers, and large companies in more than 30 countries on four continents.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI
What is dynamic programming
TL;DR: Sequence alignment methods often use something called a 'dynamic programming' algorithm, which can be a good idea or a bad idea, depending on the method used.
Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.