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Advances in Probabilistic Graphical Models (Hardcover, 2007 ed.): Peter Lucas, Jose A. Gamez, Antonio Salmeron Cerdan Advances in Probabilistic Graphical Models (Hardcover, 2007 ed.)
Peter Lucas, Jose A. Gamez, Antonio Salmeron Cerdan
R2,848 Discovery Miles 28 480 Ships in 10 - 15 working days

This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

Advances in Bayesian Networks (Hardcover, 2004 ed.): Jose A. Gamez, Serafin Moral, Antonio Salmeron Cerdan Advances in Bayesian Networks (Hardcover, 2004 ed.)
Jose A. Gamez, Serafin Moral, Antonio Salmeron Cerdan
R2,986 Discovery Miles 29 860 Ships in 10 - 15 working days

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

Advances in Bayesian Networks (Paperback, Softcover reprint of hardcover 1st ed. 2004): Jose A. Gamez, Serafin Moral, Antonio... Advances in Bayesian Networks (Paperback, Softcover reprint of hardcover 1st ed. 2004)
Jose A. Gamez, Serafin Moral, Antonio Salmeron Cerdan
R2,803 Discovery Miles 28 030 Ships in 10 - 15 working days

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

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