目錄
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
References
Part III Decision Models
10 Decision Graphs
10.1 Introduction
10.2 Decision Theory
10.2.1 Fundamentals
10.3 Decision Trees
10.4 Influence Diagrams
10.4.1 Modeling
10.4.2 Evaluation
10.4.3 Extensions
10.5 Applications
10.5.1 Decision-Theoretic Caregiver
10.6 Additional Reading
10.7 Exercises
References
11 Markov Decision Processes
11.1 Introduction
11.2 Modeling
11.3 Evaluation
11.3.1 Value Iteration
11.3.2 Policy Iteration
11.4 Factored MDPs
11.4.1 Abstraction
11.4.2 Decomposition
11.5 Partially Observable Markov Decision Processes
11.6 Applications
11.6.1 Power Plant Operation
11.6.2 Robot Task Coordination
11.7 Additional Reading
11.8 Exercises
References
Part IV Relational and Causal Models
12 Relational Probabilistic Graphical Models
12.1 Introduction
12.2 Logic
12.2.1 Propositional Logic
12.2.2 First-Order Predicate Logic
12.3 Probabilistic Relational Models
12.3.1 Inference
12.3.2 Learning
12.4 Markov Logic Networks
12.4.1 Inference
12.4.2 Learning
12.5 Applications
12.5.1 Student Modeling
12.6 Probabilistic Relational Student Model
12.6.1 Visual Grammars
12.7 Additional Reading
12.8 Exercises
Reference
13 Graphical Causal Models
13.1 Introduction
13.2 Causal Bayesian Networks
13.3 Causal Reasoning
13.3.1 Prediction
13.3.2 Counterfactuals
13.4 Learning Causal Models
13.5 Applications
13.5.1 Learning a Causal Model for ADHD
13.6 Additional Reading
13.7 Exercises
References
Glossary
Index
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