TY - BOOK AU - Koller,Daphne AU - Friedman,Nir TI - Probabilistic graphical models : principles and techniques SN - 9780262013192 (hardcover : alk. paper) U1 - 519.5420285 PY - 2009/// CY - Cambridge, MA PB - MIT Press KW - Graphical modeling (Statistics) KW - Bayesian statistical decision theory KW - Graphic methods KW - Modèles graphiques (Statistique) N1 - 1. Introduction -- 2. Foundations -- I. Representation -- 3. Bayesian Network Representation -- 4. Undirected Graphical Models -- 5. Local Probabilistic Models -- 6. Template-Based Representations -- 7. Gaussian Network Models -- 8. Exponential Family -- II. Inference -- 9. Exact Inference: Variable Elimination -- 10. Exact Inference: Clique Trees -- 11. Inference as Optimization -- 12. Particle-Based Approximate Inference -- 13. MAP Inference -- 14. Inference in Hybrid Networks -- 15. Inference in Temporal Models -- III. Learning -- 16. Learning Graphical Models: Overview -- 17. Parameter Estimation -- 18. Structure Learning in Bayesian Networks -- 19. Partially Observed Data -- 20. Learning Undirected Models -- IV. Actions and Decisions -- 21. Causality -- 22. Utilities and Decisions -- 23. Structured Decision Problems -- 24. Epilogue -- A. Background Material N2 - A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions ER -