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Condition: New. pp. x + 396 1st Edition.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Condition: New. 2nd ed. 2021 edition NO-PA16APR2015-KAP.
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Add to basketCondition: 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.
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Add to basketPaperback. Condition: Brand New. reprint edition. 277 pages. 9.25x6.10x0.63 inches. In Stock.
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Advances in Probabilistic Graphical Models (Hardcover)
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2007
ISBN 10: 354068994X ISBN 13: 9783540689942
Language: English
Hardcover. Condition: new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Advances in Probabilistic Graphical Models (Studies in Fuzziness and Soft Computing, 213)
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Condition: New. pp. x + 396.
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Advances in Probabilistic Graphical Models
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2007
ISBN 10: 354068994X ISBN 13: 9783540689942
Language: English
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Add to basketCondition: New. 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. . . . .
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Advances in Probabilistic Graphical Models
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2007
ISBN 10: 354068994X ISBN 13: 9783540689942
Language: English
Condition: New. 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.
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Advances in Probabilistic Graphical Models
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2007, 2007
ISBN 10: 354068994X ISBN 13: 9783540689942
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
US$ 129.04
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Add to basketBuch. Condition: 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.
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Advances in Probabilistic Graphical Models
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642088546 ISBN 13: 9783642088544
Language: English
US$ 129.04
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Add to basketTaschenbuch. Condition: 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.
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Advances in Probabilistic Graphical Models
Published by Springer Berlin Heidelberg, 2007
ISBN 10: 354068994X ISBN 13: 9783540689942
Language: English
US$ 129.04
Convert currencyUS$ 74.69 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketBuch. Condition: 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.
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US$ 187.23
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Add to basketPaperback. Condition: Brand New. reprint edition. 406 pages. 9.00x6.00x0.96 inches. In Stock.
