Learning Bayesian Networks (Hardback)

Condition

VERY GOOD: This book is in very good condition, showing only slight signs of use and wear.

Product Details

This book is part of the Prentice Hall Series in Artificial Intelligence .

Continued from the back cover: "This book serves as a key textbook or reference for anyone with an interest in probabilistic modeling in the fields of computer science, computer engineering, and electrical engineering. This text is also a valuable supplemental resource for courses on expert systems, machine learning, and artificial intelligence.

"Appropriate for classroom teaching or self-instruction, the text is organized to provide fundamental concepts in an accessible, practical format. Beginning with a basic theoretical introduction, the author then provides a comprehensive discussion of inference, methods of learning, and applications based on Bayesian networks and beyond.

"Learning Bayesian Networks:

Chapters

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

IV. Applications

12. Applications

The book ends with Bibliography and Index.