Errors or Clarifications of the Book

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Chapter 2. Foundations

Chapter 3. The Bayesian Network Representation

Chapter 4. Local Probabilistic Models

Chapter 5. Undirected Graphical Models

Chapter 6. Models With Higher-Level Structure

Chapter 9. Inference with Graphical Models: Overview

Chapter 10. Exact Inference: Variable Elimination

Chapter 11. Exact Inference: Clique Trees

Chapter 12. Global Approximate Inference: Inference as Optimization

Chapter 13. Particle-Based Approximate Inference

Chapter 15. Inference in Temporal Models

Chapter 18. Parameter Estimation

Chapter 19. Structure Learning in Bayesian Networks

Chapter 20. Partially Observed Data

Chapter 21. Learning Undirected Models

Chapter 22. Causality

Chapter 21. Utilities and Decisions