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Learning Bayesian Networks Paperback Richard E. Neapolitan
Free US Delivery | ISBN:0130125342
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ApproximatelyS$ 69.12
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“Former library book; may include library markings. Used book that is in clean, average condition ”... Read moreabout condition
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Item specifics
- Condition
- Good
- Seller Notes
- Features
- EX-LIBRARY
- Book Title
- Learning Bayesian Networks Paperback Richard E. Neapolitan
- ISBN
- 9780130125347
About this product
Product Identifiers
Publisher
Prentice Hall PTR
ISBN-10
0130125342
ISBN-13
9780130125347
eBay Product ID (ePID)
2318403
Product Key Features
Number of Pages
696 Pages
Language
English
Publication Name
Learning Bayesian Networks
Subject
Neural Networks, Probability & Statistics / Bayesian Analysis
Publication Year
2019
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Height
1.4 in
Item Weight
38.3 Oz
Item Length
9.1 in
Item Width
7.1 in
Additional Product Features
Intended Audience
College Audience
LCCN
2004-296044
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
519.5/42
Table Of Content
Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference: Discrete Variables. 4. More Inference Algorithms. 5. Influence Diagrams. III. LEARNING. 6. Parameter Learning: Binary Variables. 7. More Parameter Learning. 8. Bayesian Structure Learning. 9. Approximate Bayesian Structure Learning. 10. Constraint-Based Learning. 11. More Structure Learning. IV. APPICATIONS. 12. Applications. Bibliography. Index.
Synopsis
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists., 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.
LC Classification Number
QA279.5
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- r***r (2237)- Feedback left by buyer.Past monthVerified purchaseThank you for the quick delivery
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