Titlebook: Probabilistic Graphical Models; Principles and Appli Luis Enrique Sucar Textbook 20151st edition Springer-Verlag London 2015 Bayesian Class

MOAN

Probability Theory including the concept of conditional independence and Bayes rule. Third, an overview of random variables and some important distributions are described. Lastly, the basics of information theory are presented.

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Markov Decision Processestion based on graphical models to solve very large MDPs. An introduction to partially observable MDPs (POMDPs) is also included. The chapter concludes by describing two applications of MDPs: power plant control and service robot task coordination.

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Relational Probabilistic Graphical Modelselational probabilistic formalisms are described: probabilistic relational models and Markov logic networks. Finally, the application of the two previous approaches is illustrated in two domains, student modeling for a virtual laboratory and visual object recognition based on symbol-relational grammars.

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SEED

Luis Enrique Sucar important factors that distinguish between successful and less successful CEE countries during their transition period? What is a possible lesson from the transition process in the CEE countries for the contemporary European periphery? The first part of this chapter contains initial remarks, termin

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