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E-raamat: Advances in Bayesian Networks

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In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Foundations
Hypercausality, Randomisation Local and Global Independence
1(18)
Alireza Daneshkhah
Jim. Q. Smith
Introduction
1(3)
Relationships Between Causality And Parameter Independence
4(6)
The Multicausal Essential Graph
10(4)
Discussion
14(5)
References
17(2)
Interface Verification for Multiagent Probabilistic Inference
19(20)
Y. Xiang
X. Chen
Introduction
19(1)
Overview of MAMSBNs
20(3)
The Issue of Cooperative Verification
23(1)
Checking Private Parents
24(1)
Processing Public Parents
25(8)
Cooperative Verification in a General Hypertree
33(1)
Complexity
34(1)
Alternative Methods of Verification
35(2)
Conclusion
37(2)
References
38(1)
Propagation
Optimal Time-Space Tradeoff In Probabilistic Inference
39(18)
David Allen
Adnan Darwiche
Introduction
39(1)
Any-Space Inference
40(4)
The Cache Allocation Problem
44(4)
Time-Space Tradeoff
48(6)
Conclusions
54(3)
References
55(2)
Hierarchical Junction Trees
57(20)
Roberto O. Puch
Jim Q. Smith
Concha Bielza
Introduction
57(2)
Bayesian Networks
59(2)
Junction Trees
61(1)
Forecasting in the Dynamic Setting Using Junction Trees
62(2)
Hierarchical Junction Trees
64(10)
Conclusions
74(3)
References
74(3)
Algorithms for Approximate Probability Propagation in Bayesian Networks
77(24)
Andres Cano
Serafin Moral
Antonio Salmeron
Approximate Probability Propagation
77(1)
The Complexity of Probability Propagation
78(1)
The Variable Elimination Algorithm
79(2)
Shenoy-Shafer Propagation
81(7)
Monte Carlo Algorithms for Probability Propagation
88(9)
Conclusions
97(4)
References
97(4)
Abductive Inference in Bayesian Networks: A Review
101(20)
Jose A. Gamez
Introduction
101(1)
Abductive Inference in Probabilistic Reasoning
102(3)
Solving Total Abduction (MPE) in BNs
105(4)
Solving Partial Abduction (MAP) in BNs
109(6)
Complexity Results
115(1)
Conclusions
116(5)
References
117(4)
Influence Diagrams
Causal Models, Value of Intervention, and Search for Opportunities
121(16)
Tsai-Ching Lu
Marek J. Druzdzel
Introduction
121(2)
Causal Models
123(3)
Augmented Models
126(2)
Value of Intervention
128(2)
Search for Opportunities
130(3)
Non-intervening Action
133(1)
Discussion
133(4)
References
135(2)
Advances in Decision Graphs
137(24)
Thomas D. Nielsen
Finn V. Jensen
Introduction
137(1)
Influence Diagrams
138(4)
Modeling Decision Problems
142(8)
Evaluating Decision Problems
150(4)
Model Analysis
154(7)
References
156(5)
Real-World Applications of Influence Diagrams
161(20)
Manuel Gomez
Decision Making Under Uncertainty
161(1)
Artificial Intelligence, Expert Systems and Decision Support Systems
162(1)
Techniques to Build Complex DSSs
163(4)
Real World Applications
167(6)
Conclusions
173(8)
References
173(8)
Learning
Learning Bayesian Networks by Floating Search Methods
181(20)
Rosa Blanco
Inaki Inza
Pedro Larranaga
Introduction
181(1)
Learning Bayesian Networks by a Score+search Approach
182(5)
Floating Methods to Learn Bayesian Networks Structures
187(4)
Experimental Results
191(4)
Conclusions
195(6)
References
196(5)
A Graphical Meta-Model for Reasoning about Bayesian Network Structure
201(16)
Luis M. de Campos
Jose A. Gamez
J. Miguel Puerta
Introduction
201(1)
Learning Bayesian Networks
202(4)
Learning a Graphical Meta-Model
206(3)
Examples
209(3)
Concluding Remarks
212(5)
References
214(3)
Restricted Bayesian Network Structure Learning
217(18)
Peter J.F. Lucas
Introduction
217(2)
Background
219(3)
FANs: Forest-Augmented Bayesian Networks
222(3)
Evaluation
225(5)
Discussion
230(5)
References
232(3)
Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm
235(20)
Kristian Kersting
Niels Landwehr
Introduction
235(2)
Bayesian Networks
237(1)
Basic Maximum Likelihood Estimation
238(3)
``EM-hard'' and ``EM-easy'' Problems
241(1)
(Scaled) Conjugate Gradients
241(3)
Experiments
244(8)
Related Work
252(1)
Conclusions
252(3)
References
253(2)
Learning Essential Graph Markov Models from Data
255(16)
Robert Castelo
Michael D. Perlman
Introduction
255(1)
Background Concepts
256(1)
Factorization of a Multivariate Distribution According to an Essential Graph
257(2)
Bayesian Scoring Metric for Multinomial Data
259(4)
Equivalence with Respect to Other Factorizations
263(1)
Local Computations and Bayes Factors
264(3)
Concluding Remarks
267(4)
References
268(3)
Applications
Fast Propagation Algorithms for Singly Connected Networks and their Applications to Information Retrieval
271(18)
Luis M. de Campos
Juan M. Fernandez-Luna
Juan F. Huete
Introduction
271(2)
Preliminaries: Information Retrieval
273(1)
The Bayesian Network Retrieval Model
273(3)
Proposals for Reducing the Propagation Time
276(3)
Experiments and Results
279(3)
Related works
282(4)
Concluding Remarks
286(3)
References
287(2)
Continuous Speech Recognition Using Dynamic Bayesian Networks: A Fast Decoding Algorithm
289(20)
Murat Deviren
Khalid Daoudi
Introduction
289(1)
Dynamic Bayesian Networks
290(4)
Structure Search Class
294(2)
Learning Algorithm
296(3)
Decoding Algorithm
299(5)
Experiments
304(5)
References
307(2)
Applications of Bayesian Networks in Meteorology
309(18)
Rafael Cano
Carmen Sordo
Jose M. Gutierrez
Introduction
309(1)
Area of Study and Available Data
310(2)
Some Common Problems in Meteorology
312(2)
Bayesian Networks. Learning from Data
314(6)
Applications of Bayesian Networks
320(7)
References
327


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