Neapolitan, Richard E. <br/><br/>
Learning Bayesian networks / 
Richard E. Neapolitan. 
 - Upper Saddle River, N.J. :  Pearson/Prentice Hall,  c2004. 
 - xv, 674 p. :  ill. ;  24 cm. 
<br/><br/>ncludes bibliographical references (p. 647-666) and index. 
<br/><br/>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. 
<br/><br/><label>ISBN: </label>0130125342 (hbk) 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Bayesian statistical decision theory.<br/>Machine learning.<br/>Neural networks (Computer science)
<br/><br/><label>Dewey Class. No.: </label>519.542  / NEA