Learning bayesian networks Neapolitan, Richard E.

By:

Neapolitan, Richard E

Material type: Text

TextSeries: Prentice Hall Series in Artificial IntelligencePublication details: Upper Saddle River Pearson Prentice Hall 2004Description: xv, 674 pISBN:

9780130125347

Subject(s):

DDC classification:

519.542 N3L3

Summary: 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. (http://www.pearsonhighered.com/educator/product/Learning-Bayesian-Networks/9780130125347.page)

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad General Stacks	Non-fiction	519.542 N3L3 (Browse shelf(Opens below))	Available		188662

Total holds: 0

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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.
(http://www.pearsonhighered.com/educator/product/Learning-Bayesian-Networks/9780130125347.page)

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