Koller, Daphne <br/><br/>
Probabilistic Graphical Models :  Principles and Techniques / 
Daphne Koller, Nir Friedman 
 - Cambridge, MA :  MIT Press,  cop. 2009 
 - xxxv, 1231 s. :  ill. 
 - Adaptive computation and machine learning .
<br/><br/>Introduction (Page-1), Foundations (Page-15),  Bayesian Network Representation (Page-45), Undirected Graphical Models (Page-103), Local Probabilistic Models (Page-157), Template-Based Representations (Page-199), Gaussian Network Models (Page-247), Exponential Family (Page-261), Exact Inference: Variable Elimination (Page-287), Exact Inference: Clique Trees (Page-345), Inference as Optimization (Page-381), Particle-Based Approximate Inference (Page-487), MAP Inference (Page-551), Inference in Hybrid Networks (Page-605), Inference in Temporal Models (Page-651), Learning Graphical Models: Overview (Page-697), Parameter Estimation (Page-717), Structure Learning in Bayesian Networks (Page-783), Partially Observed Data (Page-849), Learning Undirected Models (Page-943), Causality (Page-1009), Utilities and Decisions (Page-1059), Structured Decision Problems (Page-1085), 
<br/><br/><label>ISBN: </label>0-262-01319-3 (hardcover : alk. paper) 978-0-262-01319-2 (hardcover : alk. paper) 
<br/><br/><label>LCCN: </label>2009008615 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Bayesian statistical decision theory--Graphic methods.<br/>Beslutsteori--matematisk statistik<br/>Graphical modeling (Statistics)<br/>Probability<br/>Sannolikhetskalkyl<br/>Statistisk inferens
<br/><br/><label>Dewey Class. No.: </label>519.54202,KOL