Ebook: Advances in Bayesian Networks
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics), Pattern Recognition, Probability Theory and Stochastic Processes, Statistical Theory and Methods, Statistics for Engineering Physics Compu
- Series: Studies in Fuzziness and Soft Computing 146
- Year: 2004
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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
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
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
Content:
Front Matter....Pages I-XI
Hypercausality, Randomisation, and Local and Global Independence....Pages 1-18
Interface Verification for Multiagent Probabilistic Inference....Pages 19-38
Optimal Time—Space Tradeoff In Probabilistic Inference....Pages 39-55
Hierarchical Junction Trees: Conditional Independence Preservation and Forecasting in Dynamic Bayesian Networks with Heterogeneous Evolution....Pages 57-75
Algorithms for Approximate Probability Propagation in Bayesian Networks....Pages 77-99
Abductive Inference in Bayesian Networks: A Review....Pages 101-120
Causal Models, Value of Intervention, and Search for Opportunities....Pages 121-135
Advances in Decision Graphs....Pages 137-159
Real-World Applications of Influence Diagrams....Pages 161-180
Learning Bayesian Networks by Floating Search Methods....Pages 181-200
A Graphical Meta-Model for Reasoning about Bayesian Network Structure....Pages 201-216
Restricted Bayesian Network Structure Learning....Pages 217-234
Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm....Pages 235-254
Learning Essential Graph Markov Models from Data....Pages 255-269
Fast Propagation Algorithms for Singly Connected Networks and their Applications to Information Retrieval....Pages 271-288
Continuous Speech Recognition Using Dynamic Bayesian Networks: A Fast Decoding Algorithm....Pages 289-308
Applications of Bayesian Networks in Meteorology....Pages 309-328
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
Content:
Front Matter....Pages I-XI
Hypercausality, Randomisation, and Local and Global Independence....Pages 1-18
Interface Verification for Multiagent Probabilistic Inference....Pages 19-38
Optimal Time—Space Tradeoff In Probabilistic Inference....Pages 39-55
Hierarchical Junction Trees: Conditional Independence Preservation and Forecasting in Dynamic Bayesian Networks with Heterogeneous Evolution....Pages 57-75
Algorithms for Approximate Probability Propagation in Bayesian Networks....Pages 77-99
Abductive Inference in Bayesian Networks: A Review....Pages 101-120
Causal Models, Value of Intervention, and Search for Opportunities....Pages 121-135
Advances in Decision Graphs....Pages 137-159
Real-World Applications of Influence Diagrams....Pages 161-180
Learning Bayesian Networks by Floating Search Methods....Pages 181-200
A Graphical Meta-Model for Reasoning about Bayesian Network Structure....Pages 201-216
Restricted Bayesian Network Structure Learning....Pages 217-234
Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm....Pages 235-254
Learning Essential Graph Markov Models from Data....Pages 255-269
Fast Propagation Algorithms for Singly Connected Networks and their Applications to Information Retrieval....Pages 271-288
Continuous Speech Recognition Using Dynamic Bayesian Networks: A Fast Decoding Algorithm....Pages 289-308
Applications of Bayesian Networks in Meteorology....Pages 309-328
....