Probabilistic graphical models : principles and techniques / Daphne Koller, Nir Friedman.

Author/creator Koller, Daphne author.
Other author Friedman, Nir, author.
Format Book
PublicationCambridge, Massachusetts : The MIT Press, [2009]
Copyright Date©2009
Descriptionxxxv, 1,233 pages : illustrations ; 24 cm.
Subjects
Graphical modeling (Statistics)
Bayesian statistical decision theory--Graphic methods.
Modèles graphiques (Statistique)
Théorie de la décision bayésienne--Méthodes graphiques.

SeriesAdaptive computation and machine learning
Adaptive computation and machine learning. ^A474767
Contents 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.
Bibliography noteIncludes bibliographical references (pages 1173-1209) and indexes.
Issued in other formOnline version: Koller, Daphne. Probabilistic graphical models. Cambridge, Massachusetts : The MIT Press, [2009] 9780262259842
LCCN 2009008615
ISBN0262013193 (hardcover ; alk. paper)
ISBN9780262013192 (hardcover ; alk. paper)

Availability

Library Location Call Number Status Item Actions
Joyner General Stacks QA279.5 .K65 2009 ✔ Available Place Hold