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Chapter 1. Foundations
Chapter 2. The Bayesian Network Representation
Chapter 3. Undirected Graphical Models
Chapter 4. Local Probabilistic Models
Chapter 5. Template-Based Representations
Chapter 6. Exact Inference: Variable Elimination
Chapter 7. Exact Inference: Clique Trees
Chapter 8. Inference as Optimization
Chapter 9. Particle-Based Approximate Inference
Chapter 10. MAP Inference
Chapter 11. Inference in Temporal Models
Chapter 12. Learning Graphical Models: Overview
Chapter 13. Parameter Estimation
Chapter 14. Structure Learning in Bayesian Networks
Chapter 15. Partially Observed Data
Chapter 16. Learning Undirected Models
Chapter 17. Causality
Chapter 18. Utilities and Decisions
Chapter 19. Structured Decision Problems
Chapter 20. Epilogue
Appendix
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