Advanced methodologies for Bayesian networks [electronic resource] : second international workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / Joe Suzuki, Maomi Ueno (eds.).
2015
Q334
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Title
Advanced methodologies for Bayesian networks [electronic resource] : second international workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / Joe Suzuki, Maomi Ueno (eds.).
Meeting Name
ISBN
9783319283791 electronic book
3319283790 electronic book
9783319283784
3319283790 electronic book
9783319283784
Published
Cham : Springer, 2015.
Language
English
Description
1 online resource (xviii, 265 pages) : color illustrations.
Item Number
10.1007/978-3-319-28379-1 doi
Call Number
Q334
Dewey Decimal Classification
006.3
Summary
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.
Note
International conference proceedings.
Includes author index.
Includes author index.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 15, 2016).
Added Author
Series
Lecture notes in computer science. Lecture notes in artificial intelligence ; 9505.
LNCS sublibrary. SL 7, Artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
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Table of Contents
Effectiveness of graphical models including modeling. Reasoning, model selection
Logic-probability relations
Causality. Applying graphical models in real world settings
Scalability
Incremental learning.-Parallelization.
Logic-probability relations
Causality. Applying graphical models in real world settings
Scalability
Incremental learning.-Parallelization.