Advances in Probabilistic Graphical Models

. Ed(s): Lucas, Peter; Gamez, Jose A.; Salmeron, Antonio

ISBN 10: 354068994X / ISBN 13: 9783540689942

Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2007

Riguardo questo articolo

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. Editor(s): Lucas, Peter; Gamez, Jose A.; Salmeron, Antonio. Series: Studies in Fuzziness and Soft Computing. Num Pages: 396 pages, 26 black & white tables, biography. BIC Classification: PBT; PBV; PBWH. Category: (P) Professional & Vocational. Dimension: 235 x 155. . . 2007. Hardback. . . . . Books ship from the US and Ireland.

Codice articolo V9783540689942

Dati bibliografici

• Titolo: Advances in Probabilistic Graphical Models
• Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 354068994X
• ISBN 13: 9783540689942
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

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.