Advances in Probabilistic Graphical Models
Language: English
Published by Springer, 2007
Series: Book 36 of 183 - Studies in Fuzziness and Soft Computing
- Hardcover
- Used

Seller: Anybook.com, Lincoln, United KingdomAnybook.com
AbeBooks seller since December 22, 1999
Condition: Used - Good
US$ 45.53
Quantity: 1 available
Add to basketItem description from seller
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.
Seller Inventory # 4148764
- Title
- Advances in Probabilistic Graphical Models
- Author
- Gamez, Jose A. (Ed.)
- Publisher
- Springer
- Publication year
- 2007
- Condition
- Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 354068994X
- ISBN 13
- 9783540689942
- Item weight
- 750 grams
- Series
- Book 36 of 183: Studies in Fuzziness and Soft Computing
- Seller catalogs
- Science & Technology
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Anybook.com
Lincoln, United Kingdom
AbeBooks seller since December 22, 1999
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