Screen LibraryProbabilistic graphical models：principles and techniques
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| Main Authors: | |
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| Group Author: | |
| Published: |
MIT Press,
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| Publisher Address: | Cambridge, MA |
| Publication Dates: | c2009. |
| Literature type: | Book |
| Language: | English |
| Series: |
Adaptive computation and machine learning |
| Subjects: | |
| Carrier Form: | xxxv, 1231 p.: ill. ; 24 cm. |
| ISBN: |
9780262013192 (hardcover : alk. paper) 0262013193 (hardcover : alk. paper) |
| Index Number: | O212 |
| CLC: | O212.8 |
| Call Number: | O212.8/K814 |
| Contents: |
Includes bibliographical references (p. [1171]-1207) and indexes. 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. |