Probabilistic Graphical Models:
Principles and Techniques

 
  1. Chapter 1: Introduction

    • Chapter 2: Foundations

      • Chapter 3: The Bayesian Network Representation

        • Chapter 4: Undirected Graphical Models

          • Chapter 5: Local Probabilistic Models

            • Chapter 6: Template-Based Representations

              • Chapter 7: Gaussian Network Models

                • Chapter 8: The Exponential Family

                  • Chapter 9: Variable Elimination

                    • Chapter 10: Clique Trees

                      • Chapter 11: Inference as Optimization

                        • Chapter 12: Particle-Based Approximate Inference

                          • Chapter 13: MAP Inference

                            • Chapter 14: Inference in Hybrid Networks

                              • Chapter 15: Inference in Temporal Models

                                • Chapter 16: Learning Graphical Models: Overview

                                  • Chapter 17: Parameter Estimation

                                    • Chapter 18: Structure Learning in Bayesian Networks

                                      • Chapter 19: Partially Observed Data

                                        • Chapter 20: Learning Undirected Models

                                          • Chapter 21: Causality

                                            • Chapter 22: Utilities and Decisions

                                              • Chapter 23: Structured Decision Problems

                                                • Appendix