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E-raamat: Probabilistic Graphical Models: Principles and Applications

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Probabilistic Graphical Models: Principles and ApplicationsSuurem pilt
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This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
Part I Fundamentals
1 Introduction
3(12)
1.1 Uncertainty
3(1)
1.1.1 Effects of Uncertainty
3(1)
1.2 A Brief History
4(1)
1.3 Basic Probabilistic Models
5(3)
1.3.1 An Example
6(2)
1.4 Probabilistic Graphical Models
8(2)
1.5 Representation, Inference, and Learning
10(1)
1.6 Applications
11(1)
1.7 Overview of the Book
12(1)
1.8 Additional Reading
13(1)
References
13(2)
2 Probability Theory
15(12)
2.1 Introduction
15(1)
2.2 Basic Rules
16(2)
2.3 Random Variables
18(5)
2.3.1 Two-Dimensional Random Variables
21(2)
2.4 Information Theory
23(2)
2.5 Additional Reading
25(1)
2.6 Exercises
25(1)
References
26(1)
3 Graph Theory
27(14)
3.1 Definitions
27(1)
3.2 Types of Graphs
28(1)
3.3 Trajectories and Circuits
29(1)
3.4 Graph Isomorphism
30(1)
3.5 Trees
31(2)
3.6 Cliques
33(1)
3.7 Perfect Ordering
34(1)
3.8 Ordering and Triangulation Algorithms
35(2)
3.8.1 Maximum Cardinality Search
35(1)
3.8.2 Graph Filling
36(1)
3.9 Additional Reading
37(1)
3.10 Exercises
37(1)
References
38(3)
Part II Probabilistic Models
4 Bayesian Classifiers
41(22)
4.1 Introduction
41(1)
4.1.1 Classifier Evaluation
42(1)
4.2 Bayesian Classifier
42(4)
4.2.1 Naive Bayes Classifier
43(3)
4.3 Alternative Models: TAN, BAN
46(2)
4.4 Semi-Naive Bayesian Classifiers
48(2)
4.5 Multidimensional Bayesian Classifiers
50(4)
4.5.1 Multidimensional Bayesian Network Classifiers
51(1)
4.5.2 Bayesian Chain Classifiers
52(2)
4.6 Hierarchical Classification
54(2)
4.6.1 Chained Path Evaluation
55(1)
4.7 Applications
56(4)
4.7.1 Visual Skin Detection
56(2)
4.7.2 HIV Drug Selection
58(2)
4.8 Additional Reading
60(1)
4.9 Exercises
60(1)
References
61(2)
5 Hidden Markov Models
63(20)
5.1 Introduction
63(1)
5.2 Markov Chains
64(4)
5.2.1 Parameter Estimation
66(1)
5.2.2 Convergence
67(1)
5.3 Hidden Markov Models
68(9)
5.3.1 Evaluation
70(2)
5.3.2 State Estimation
72(2)
5.3.3 Learning
74(2)
5.3.4 Extensions
76(1)
5.4 Applications
77(3)
5.4.1 PageRank
78(1)
5.4.2 Gesture Recognition
78(2)
5.5 Additional Reading
80(1)
5.6 Exercises
80(1)
References
81(2)
6 Markov Random Fields
83(18)
6.1 Introduction
83(1)
6.2 Markov Networks
84(4)
6.2.1 Regular Markov Random Fields
86(2)
6.3 Gibbs Random Fields
88(1)
6.4 Inference
88(2)
6.5 Parameter Estimation
90(2)
6.5.1 Parameter Estimation with Labeled Data
90(2)
6.6 Conditional Random Fields
92(1)
6.7 Applications
93(5)
6.7.1 Image Smoothing
93(2)
6.7.2 Improving Image Annotation
95(3)
6.8 Additional Reading
98(1)
6.9 Exercises
98(1)
References
99(2)
7 Bayesian Networks: Representation and Inference
101(36)
7.1 Introduction
101(1)
7.2 Representation
102(9)
7.2.1 Structure
103(3)
7.2.2 Parameters
106(5)
7.3 Inference
111(18)
7.3.1 Singly Connected Networks: Belief Propagation
112(4)
7.3.2 Multiple Connected Networks
116(8)
7.3.3 Approximate Inference
124(2)
7.3.4 Most Probable Explanation
126(1)
7.3.5 Continuous Variables
127(2)
7.4 Applications
129(5)
7.4.1 Information Validation
129(3)
7.4.2 Reliability Analysis
132(2)
7.5 Additional Reading
134(1)
7.6 Exercises
135(1)
References
135(2)
8 Bayesian Networks: Learning
137(24)
8.1 Introduction
137(1)
8.2 Parameter Learning
137(6)
8.2.1 Smoothing
137(1)
8.2.2 Parameter Uncertainty
138(1)
8.2.3 Missing Data
139(3)
8.2.4 Discretization
142(1)
8.3 Structure Learning
143(10)
8.3.1 Tree Learning
144(2)
8.3.2 Learning a Polytree
146(1)
8.3.3 Search and Score Techniques
147(5)
8.3.4 Independence Tests Techniques
152(1)
8.4 Combining Expert Knowledge and Data
153(1)
8.5 Applications
154(3)
8.5.1 Air Pollution Model for Mexico City
154(3)
8.6 Additional Reading
157(1)
8.7 Exercises
157(2)
References
159(2)
9 Dynamic and Temporal Bayesian Networks
161(20)
9.1 Introduction
161(1)
9.2 Dynamic Bayesian Networks
161(3)
9.2.1 Inference
163(1)
9.2.2 Learning
163(1)
9.3 Temporal Event Networks
164(5)
9.3.1 Temporal Nodes Bayesian Networks
165(4)
9.4 Applications
169(7)
9.4.1 DBN: Gesture Recognition
169(4)
9.4.2 TNBN: Predicting HIV Mutational Pathways
173(3)
9.5 Additional Reading
176(1)
9.6 Exercises
176(1)
References
177(4)
Part III Decision Models
10 Decision Graphs
181(18)
10.1 Introduction
181(1)
10.2 Decision Theory
181(4)
10.2.1 Fundamentals
182(3)
10.3 Decision Trees
185(2)
10.4 Influence Diagrams
187(6)
10.4.1 Modeling
187(1)
10.4.2 Evaluation
188(4)
10.4.3 Extensions
192(1)
10.5 Applications
193(3)
10.5.1 Decision-Theoretic Caregiver
193(3)
10.6 Additional Reading
196(1)
10.7 Exercises
196(1)
References
197(2)
11 Markov Decision Processes
199(20)
11.1 Introduction
199(1)
11.2 Modeling
199(3)
11.3 Evaluation
202(2)
11.3.1 Value Iteration
202(1)
11.3.2 Policy Iteration
203(1)
11.4 Factored MDPs
204(3)
11.4.1 Abstraction
206(1)
11.4.2 Decomposition
206(1)
11.5 Partially Observable Markov Decision Processes
207(1)
11.6 Applications
208(6)
11.6.1 Power Plant Operation
208(2)
11.6.2 Robot Task Coordination
210(4)
11.7 Additional Reading
214(1)
11.8 Exercises
214(1)
References
215(4)
Part IV Relational and Causal Models
12 Relational Probabilistic Graphical Models
219(18)
12.1 Introduction
219(1)
12.2 Logic
220(3)
12.2.1 Propositional Logic
220(1)
12.2.2 First-Order Predicate Logic
221(2)
12.3 Probabilistic Relational Models
223(2)
12.3.1 Inference
225(1)
12.3.2 Learning
225(1)
12.4 Markov Logic Networks
225(3)
12.4.1 Inference
227(1)
12.4.2 Learning
227(1)
12.5 Applications
228(1)
12.5.1 Student Modeling
228(1)
12.6 Probabilistic Relational Student Model
228(5)
12.6.1 Visual Grammars
231(2)
12.7 Additional Reading
233(1)
12.8 Exercises
233(1)
References
234(3)
13 Graphical Causal Models
237(10)
13.1 Introduction
237(1)
13.2 Causal Bayesian Networks
238(2)
13.3 Causal Reasoning
240(2)
13.3.1 Prediction
240(1)
13.3.2 Counterfactuals
241(1)
13.4 Learning Causal Models
242(2)
13.5 Applications
244(1)
13.5.1 Learning a Causal Model for ADHD
244(1)
13.6 Additional Reading
245(1)
13.7 Exercises
245(1)
References
246(1)
Glossary 247(4)
Index 251
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