Probabilistic graphical models : principles and techniques

✍ Scribed by Daphne Koller; Nir Friedman

• Publisher: MIT Press
• Year: 2009
• Tongue: English
• Leaves: 1270
• Series: Adaptive computation and machine learning
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Contents......Page 10
Acknowledgments......Page 24
List of Figures......Page 26
List of Algorithms......Page 32
List of Boxes......Page 34
1 Introduction......Page 38
2 Foundations......Page 52
Part I - Representation......Page 80
3 The Bayesian Network Representation......Page 82
4 Undirected Graphical Models......Page 140
5 Local Probabilistic Models......Page 194
6 Template-Based Representations......Page 236
7 Gaussian Network Models......Page 284
8 The Exponential Family......Page 298
Part II - Inference......Page 322
9 Exact Inference: Variable Elimination......Page 324
10 Exact Inference: Clique Trees......Page 382
11 Inference as Optimization......Page 418
12 Particle-Based Approximate Inference......Page 524
13 MAP Inference......Page 588
14 Inference in Hybrid Networks......Page 642
15 Inference in Temporal Models......Page 688
Part III - Learning......Page 732
16 Learning Graphical Models: Overview......Page 734
17 Parameter Estimation......Page 754
18 Structure Learning in Bayesian Networks......Page 820
19 Partially Observed Data......Page 886
20 Learning Undirected Models......Page 980
Part IV - Actions and Decisions......Page 1044
21 Causality......Page 1046
22 Utilities and Decisions......Page 1096
23 Structured Decision Problems......Page 1122
24 Epilogue......Page 1170
Appendix A Background Material......Page 1174
Bibliography......Page 1210
Notation Index......Page 1248
Subject Index......Page 1252