Borgelt, Christian. <br/><br/>
Graphical models :  representations for learning, reasoning and data mining / 
Christian Borgelt, Matthias Steinbrecher & Rudolf Kruse. 
 - 2nd ed. 
 - Chichester, West Sussex, UK :  John Wiley,  2009. 
 - viii, 393 pages :  illustrations ;  24 cm. 
 - Wiley series in computational statistics .
<br/><br/>Includes bibliographical references and index. 
<br/><br/>1. Introduction<br/>2. Imprecision and Uncertainty<br/>3. Decomposition<br/>4. Graphical Representation<br/>5. Computing Projections<br/>6. Naive Classifiers<br/>7. Learning Global Structure<br/>8. Learning Local Structure<br/>9. Inductive Causation<br/>10. Visualization<br/>11. Applications<br/>A. Proofs of Theorems<br/>B. Software Tools 
<br/><br/> Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distribution 
<br/><br/><label>ISBN: </label>9780470722107 (cloth) 047072210X (cloth) 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining.<br/>Mathematical statistics--Graphic methods.
<br/><br/><label>LC Class. No.: </label>QA76.9.D343  / B67 2009