本书从工程的角度概述了概率图模型（PGMs）。书本涵盖了PGMs每种主要类别的基础知识，包括表示、推理和学习原则，并回顾了每种类型的模型在现实世界中的应用。这些应用来自广泛的学科，突出了贝叶斯分类器、隐马尔可夫模型、贝叶斯网络、动态和时间贝叶斯网络、马尔可夫随机场、影响图和马尔可夫决策过程的许多用途。本书特色：提出了包括PGMs所有主要类别的统一框架；介绍了不同技术的实际应用；该领域研究的最新发展，包括多维贝叶斯分类器、关系图模型和因果模型；每一章的末尾都附有练习、进一步阅读的建议和研究或编程项。

Part I Fundamentals
1 Introduction
1.1 Uncertainty
1.1.1 Effects of Uncertainty
1.2 A Brief History
1.3 Basic Probabilistic Models
1.3.1 An Example
1.4 Probabilistic Graphical Models
1.5 Representation, Inference, and Learning
1.6 Applications
1.7 Overview of the Book
1.8 Additional Reading
References
2 Probability Theory
2.1 Introduction
2.2 Basic Rules
2.3 Random Variables
2.3.1 Two-Dimensional Random Variables
2.4 Information Theory
2.5 Additional Reading
2.6 Exercises
Reference
3 Graph Theory
3.1 Definitions
3.2 Types of Graphs
3.3 Trajectories and Circuits
3.4 Graph Isomorphism
3.5 Trees
3.6 Cliques
3.7 Perfect Ordering
3.8 Ordering and Triangulation Algorithms
3.8.1 Maximum Cardinality Search
3.8.2 Graph Filling
3.9 Additional Reading
3.10 Exercises
Reference
Part II Probabilistic Models
4 Bayesian Classifiers
4.1 Introduction
4.1.1 Classifier Evaluation
4.2 Bayesian Classifier
4.2.1 Naive Bayes Classifier
4.3 Alternative Models: TAN, BAN
4.4 Semi-Naive Bayesian Classifiers
4.5 Multidimensional Bayesian Classifiers
4.5.1 Multidimensional Bayesian Network Classifiers
4.5.2 Bayesian Chain Classifiers
4.6 Hierarchical Classification
4.6.1 Chained Path Evaluation
4.7 Applications
4.7.1 Visual Skin Detection
4.7.2 HIV Drug Selection
4.8 Additional Reading
4.9 Exercises
References
5 Hidden Markov Models
5.1 Introduction
5.2 Markov Chains
5.2.1 Parameter Estimation
5.2.2 Convergence
5.3 Hidden Markov Models
5.3.1 Evaluation
5.3.2 State Estimation
5.3.3 Learning
5.3.4 Extensions
5.4 Applications
5.4.1 PageRank
5.4.2 Gesture Recognition
5.5 Additional Reading
5.6 Exercises
References
6 Markov Random Fields
6.1 Introduction
6.2 Markov Networks
6.2.1 Regular Markov Random Fields
6.3 Gibbs Random Fields
6.4 Inference
6.5 Parameter Estimation
6.5.1 Parameter Estimation with Labeled Data
6.6 Conditional Random Fields
6.7 Applications
6.7.1 Image Smoothing
6.7.2 Improving Image Annotation
6.8 Additional Reading
6.9 Exercises
References
7 Bayesian Networks: Representation and Inference
7.1 Introduction
7.2 Representation
7.2.1 Structure
7.2.2 Parameters
7.3 Inference
7.3.1 Singly Connected Networks: Belief Propagation
7.3.2 Multiple Connected Networks
7.3.3 Approximate Inference
7.3.4 Most Probable Explanation
7.3.5 Continuous Variables
7.4 Applications
7.4.1 Information Validation
7.4.2 Reliability Analysis
7.5 Additional Reading
7.6 Exercises
References
8 Bayesian Networks: Learning
8.1 Introduction
8.2 Parameter Learning
8.2.1 Smoothing
8.2.2 Parameter Uncertainty
8.2.3 Missing Data
8.2.4 Discretization
8.3 Structure Learning
8.3.1 Tree Learning
8.3.2 Learning a Polytree
8.3.3 Search and Score Techniques
8.3.4 Independence Tests Techniques
8.4 Combining Expert Knowledge and Data
8.5 Applications
8.5.1 Air Pollution Model for Mexico City
8.6 Additional Reading
8.7 Exercises
References
9 Dynamic and Temporal Bayesian Networks
9.1 Introduction
9.2 Dynamic Bayesian Networks
9.2.1 Inference
9.2.2 Learning
9.3 Temporal Event Networks
9.3.1 Temporal Nodes Bayesian Networks
9.4 Applications
9.4.1 DBN: Gesture Recognition
9.4.2 TNBN: Predicting HIV Mutational Pathways
9.5 Additional Reading
9.6 Exercises