TY  - GEN
AU  - Koller,Daphne
AU  - Friedman,Nir
TI  - Probabilistic Graphical Models: Principles and Techniques 
T2  - Adaptive computation and machine learning
SN  - 0-262-01319-3 (hardcover : alk. paper)
U1  - 519.54202,KOL 
PY  - 2009///
CY  - Cambridge, MA
PB  - MIT Press
KW  - Bayesian statistical decision theory
KW  - Graphic methods
KW  - Beslutsteori
KW  - matematisk statistik
KW  - Graphical modeling (Statistics)
KW  - Probability
KW  - Sannolikhetskalkyl
KW  - Statistisk inferens
N1  - 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)
ER  -