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Learning Bayesian Networks

Librairie Eyrolles - Paris 5e

Learning Bayesian Networks

Learning Bayesian Networks

688 pages, parution le 25/04/2003


For courses in Bayesian Networks or Advanced Networking focusing on Bayesian networks found in departments of Computer Science, Computer Engineering and Electrical Engineering. Also appropriate as a supplementary text in courses on Expert Systems, Machine Learning, and Artificial Intelligence where the topic of Bayesian Networks is covered.

This book provides an accessible and unified discussion of Bayesian networks. It includes discussions of topics related to the areas of artificial intelligence, expert systems and decision analysis, the fields in which Bayesian networks are frequently applied. The author discusses both methods for doing inference in Bayesian networks and influence diagrams. The book also covers the Bayesian method for learning the values of discrete and continuous parameters. Both the Bayesian and constraint-based methods for learning structure are discussed in detail.

  • Hundreds of examples and problems, allows students to test themselves.
  • Complex concepts are introduced with simple examples, eases the student into the text without being overwhelmed.
  • Separation of the statistical development of Bayesian networks from the application to causality, avoids confusion which can occur when the two are covered simultaneously.



  • Introduction to Bayesian Networks
  • More DAG/Probability Relationships
  • Inference: Discrete Variables
  • More Inference Algorithms
  • Influence Diagrams
  • Parameter Learning: Binary Variables
  • More Parameter Learning
  • Bayesian Structure Learning
  • Constraint-Based Learning
  • More Structure Learning



Caractéristiques techniques du livre "Learning Bayesian Networks"

Éditeur(s) Prentice Hall
Auteur(s) Richard E. Neapolitan
Parution 25/04/2003
Nb. de pages 688
Format 18 x 24
Couverture Relié
Poids 1180g
Intérieur Noir et Blanc
EAN13 9780130125347
ISBN13 978-0-13-012534-7


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