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Advances in Bayesian Networks

'Studies in Fuzziness and Soft Computing'. Auflage 2004. Book. Sprache: Englisch.
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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 c... weiterlesen
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Titel: Advances in Bayesian Networks

ISBN: 3540208763
EAN: 9783540208761
'Studies in Fuzziness and Soft Computing'.
Auflage 2004.
Book.
Sprache: Englisch.
Herausgegeben von José A. Gámez, Serafin Moral, Antonio Salmerón Cerdan
Springer Berlin Heidelberg

23. Februar 2004 - gebunden - 340 Seiten

Beschreibung

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.

Inhaltsverzeichnis

Hypercausality, Randomisation Local and Global Independence.- Interface Verification for Multiagent Probabilistic Inference.- Optimal Time-Space Tradeoff In Probabilistic Inference.- Hierarchical Junction Trees.- Algorithms for Approximate Probability Propagation in Bayesian Networks.- Abductive Inference in Bayesian Networks: A Review.- Causal Models, Value of Intervention, and Search for Opportunities.- Advances in Decision Graphs.- Real-World Applications 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 Algorithms for Singly Connected Networks and their Applications to Information Retrieval.- Continuous Speech Recognition Using Dynamic Bayesian Networks: A Fast Decoding Algorithm.- Applications of Bayesian Networks in Meteorology.
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