Taylor's Library

Normal view MARC view ISBD view

Learning Bayesian networks / Richard E. Neapolitan.

By: Neapolitan, Richard E.
Publisher: Upper Saddle River, N.J. : Pearson/Prentice Hall, c2004Description: xv, 674 p. : ill. ; 24 cm.ISBN: 0130125342 (hbk).Subject(s): Bayesian statistical decision theory | Machine learning | Neural networks (Computer science)DDC classification: 519.542
Contents:
Preface. - I. Basics. 1. Introduction to Bayesian Networks. 2. More DAC / 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. Applications. 12. Applications. Bibliography. - Index.
Item type Current location Call number Copy number Status Notes Date due Barcode Remark
Main Collection TU External Storage-LCS
519.542 NEA (Browse shelf) 1 Available SOCIT,15008,03,GR 1000111435 Please fill up online form at https://taylorslibrary.taylors.edu.my/services/external_storage1

ncludes bibliographical references (p. 647-666) and index.

Preface. - I. Basics. 1. Introduction to Bayesian Networks. 2. More DAC / 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. Applications. 12. Applications. Bibliography. - Index.