Innovations in Bayesian networks : theory and applications

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both t...

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Bibliographic Details
Main Author: Holmes, Dawn E.
Other Authors: Kacprzyk, Janusz, 1947- (Publishing director), Holmes, Dawn E., 19..- (Publishing director), Jain, Lakhmi C., 1946- (Publishing director)
Format: Livre numérique
Language:Anglais
Published: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Edition:1st ed. 2008.
Series:Studies in Computational Intelligence 156
Online Access:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Note: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Innovations in Bayesian Networks, Texte imprimé, 9783540873112
• Innovations in Bayesian Networks, Texte imprimé, 9783642098758
• Innovations in Bayesian Networks, Texte imprimé, 9783540850656
• Innovations in Bayesian Networks, Texte imprimé, 9783540873112
• Innovations in Bayesian Networks, Texte imprimé, 9783642098758
• Innovations in Bayesian Networks, Texte imprimé, 9783540850656
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505 1 |a to Bayesian Networks A Polemic for Bayesian Statistics A Tutorial on Learning with Bayesian Networks The Causal Interpretation of Bayesian Networks An Introduction to Bayesian Networks and Their Contemporary Applications Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks An Information-Geometric Approach to Learning Bayesian Network Topologies from Data Causal Graphical Models with Latent Variables: Learning and Inference Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem Toward a Generalized Bayesian Network A Survey of First-Order Probabilistic Models. 
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520 |a Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research. 
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