Probabilistic graphical models principles and techniques
Material type:
TextLanguage: English Language Series: Adaptive computation and machine learningPublication details: Cambridge, MA MIT Press 2009Description: xxi, 1231 p. 24 cmISBN: - 9780262013192 (hardcover : alk. paper)
- 519.5420285 KOL
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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.
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.
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