Advanced Methodologies for Bayesian Networks

Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
Cover of the book Advanced Methodologies for Bayesian Networks by , Springer International Publishing
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Author: ISBN: 9783319283791
Publisher: Springer International Publishing Publication: January 7, 2016
Imprint: Springer Language: English
Author:
ISBN: 9783319283791
Publisher: Springer International Publishing
Publication: January 7, 2016
Imprint: Springer
Language: English

This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015.

The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015.

The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.

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