Description
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:
- Includes hundreds of examples and problems
- Makes learning easy by introducing complex concepts through simple examples
- Clarifies with separate discussions on statistical development of Bayesian networks and application to causality.”
Chapters
I. Basics
- Introduction to Bayesian Networks
- More DAG/Probability Relationships
II. Inference
- Inference Discrete Variables
- More Inference Algorithms
- Influence Diagrams
III. Learning
- Parameter Learning: Binary Variables
- More Parameter Learning
- Bayesian Structure Learning
- Approximate Bayesian Structure Learning
- Constraint-Based Learning
IV. Applications
- Applications
The book ends with Bibliography and Index.