×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Learning Bayesian Networks
     

Learning Bayesian Networks

by Richard E. Neapolitan
 

ISBN-10: 0130125342

ISBN-13: 9780130125347

Pub. Date: 03/27/2003

Publisher: Pearson

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,

Overview

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.

Product Details

ISBN-13:
9780130125347
Publisher:
Pearson
Publication date:
03/27/2003
Series:
Prentice Hall Series in Artificial Intelligence
Edition description:
New Edition
Pages:
674
Product dimensions:
7.00(w) x 8.90(h) x 1.60(d)

Table of Contents

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.

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews