Advances in Bayesian networks [European Workshop on Probabilistic Graphical Models, PGM '02]
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...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Berlin, Heidelberg u.a.
Springer
2004
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| Schriftenreihe: | Studies in fuzziness and soft computing
146 |
| Schlagworte: | |
| Online Zugang: | Cover Inhaltsverzeichnis Inhaltstext Publisher description |
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| Zusammenfassung: | 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. TOC:Hypercausality, Randomisation Local and Global Independence.- Interface Verification for Multiagent Probabilistic Inference.- Optimal Time-Space Tradeoff in Probalistic Inference.- Hierarchical Junction Trees.- Algorithms for Approximate Probability Propagation in Bayesian Networks.- Abductive Inference in Bayesian Networks: A Review.- Causal Models, Value of Intervention, an Search for Opportunities.- Advances in Decision Graphs.- Real-World Application of Influence Diagrams.- Learning Bayesian Networks by Floating Search Methods.- A Graphical Meta- Model for Reasoning about Bayesian Network Structure.- Restricted Bayesian Network Structure Learning.- Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm.- Learning Essential Graph Markov Models from Data.- Fast Propagation Algrithms for Singly Connected Networks and their Applications to Information Retrieval.- Continuous Speech Recognition Using Dynamics Bayesian Networks: A Fast Decoding Algorithm.- Application of Bayesian Networks in Meteorology |
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| Beschreibung: | Some of the contributions to this book have been selected from the papers presented at the First European Workshop on Probabilistic Graphical Models (PGM'02), held in Cuenca (Spain) in November 2002 |
| Beschreibung: | XI, 328 S. graph. Darst. 24 cm |
| ISBN: | 3540208763 3-540-20876-3 |