Advances in Bayesian Networks

Author:   José A. Gámez ,  Serafin Moral ,  Antonio Salmerón Cerdan
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   Softcover reprint of hardcover 1st ed. 2004
Volume:   146
ISBN:  

9783642058851


Pages:   328
Publication Date:   15 December 2010
Format:   Paperback
Availability:   In Print   Availability explained
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Advances in Bayesian Networks


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Author:   José A. Gámez ,  Serafin Moral ,  Antonio Salmerón Cerdan
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of hardcover 1st ed. 2004
Volume:   146
Dimensions:   Width: 15.50cm , Height: 1.80cm , Length: 23.50cm
Weight:   0.528kg
ISBN:  

9783642058851


ISBN 10:   364205885
Pages:   328
Publication Date:   15 December 2010
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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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