Advances in Probabilistic Graphical Models: 213 (Studies in Fuzziness and Soft Computing) - Tapa dura

 
9783540689942: Advances in Probabilistic Graphical Models: 213 (Studies in Fuzziness and Soft Computing)
Tapa dura
ISBN 10: 354068994X ISBN 13: 9783540689942
Editorial: Springer, 2007

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2007
Idioma
Inglés
ISBN 10 
354068994X
ISBN 13 
9783540689942
Encuadernación
Tapa dura
Número de páginas
396
Editor
Salmerón Cerdan Antonio, Gámez José A., Lucas Peter
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783642088544: Advances in Probabilistic Graphical Models: 213 (Studies in Fuzziness and Soft Computing)

Edición Destacada

ISBN 10:  3642088546 ISBN 13:  9783642088544
Editorial: Springer, 2010
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