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Chapter 1: Introduction
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Chapter 2: Foundations
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Chapter 3: The Bayesian Network Representation
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Chapter 4: Undirected Graphical Models
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Chapter 5: Local Probabilistic Models
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Chapter 6: Template-Based Representations
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Chapter 7: Gaussian Network Models
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Chapter 8: The Exponential Family
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Chapter 9: Variable Elimination
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Chapter 10: Clique Trees
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Chapter 11: Inference as Optimization
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Chapter 12: Particle-Based Approximate Inference
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Chapter 13: MAP Inference
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Chapter 14: Inference in Hybrid Networks
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Chapter 15: Inference in Temporal Models
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Chapter 16: Learning Graphical Models: Overview
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Chapter 17: Parameter Estimation
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Chapter 18: Structure Learning in Bayesian Networks
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Chapter 19: Partially Observed Data
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Chapter 20: Learning Undirected Models
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Chapter 21: Causality
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Chapter 22: Utilities and Decisions
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Chapter 23: Structured Decision Problems
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Appendix
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