Titlebook: Probabilistic Graphical Models; Principles and Appli Luis Enrique Sucar Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bay

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Cosmopolitan

Causal Discoveryed, including score-based and constraint-based; as well as a technique for linear models. Finally, it includes an example of learning causal models from real world data about children with attention deficit hyperactivity disorder; and an application for discovering brain effective connectivity networks based on neuro-images.

起皱纹

NUL

Bayesian Classifiers chain classifier. Then an introduction to hierarchical classification is presented. The chapter concludes by illustrating the application of Bayesian classifiers in two domains: skin pixel detection in images and drug selection for HIV treatment.

灯丝

Markov Random Fieldsaddressed, considering the maximum likelihood estimator. Conditional random fields are also introduced. The chapter concludes with two applications of MRFs for image analysis, one for image de-noising, and the other for improving image annotation by including spatial relations.

propose

Bayesian Networks: Representation and Inferencence are introduced, including belief propagation, variable elimination, conditioning, junction trees, loopy propagation and stochastic simulation. The chapter concludes by illustrating the application of Bayesian networks in information validation and system reliability analysis.