advances probabilistic graphical models (10 Ergebnisse)

Zustand: New.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9783540689942.

Zustand: New. In.

Zustand: New. In English.

Paperback. Zustand: Brand New. reprint edition. 277 pages. 9.25x6.10x0.63 inches. In Stock.

Zustand: New. In.

Buch. Zustand: Neu. Neuware -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 conditionalindependence, 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 396 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Paperback. Zustand: Brand New. reprint edition. 406 pages. 9.00x6.00x0.96 inches. In Stock.