Advances in Probabilistic Graphical Models by Peter Lucas, José A. Gámez, Antonio Salmerón Cerdan
Estimated delivery 3-12 business days
Format Hardcover
Condition Brand New
Description Brings together important topics of research in probabilistic graphical modeling, learning from data and probabilistic inference. This title includes coverage of such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism.
Publisher Description
In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;contributions to the area are coming from computer science, mathematics, statistics and engineering.This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.
Details ISBN 354068994X ISBN-13 9783540689942 Title Advances in Probabilistic Graphical Models Author Peter Lucas, José A. Gámez, Antonio Salmerón Cerdan Format Hardcover Year 2007 Pages 386 Edition 2007th Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG GE_Item_ID:137621864; About Us
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