Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings
Author:   Joe Suzuki ,  Maomi Ueno
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   9505
ISBN:  

9783319283784


Pages:   265
Publication Date:   25 February 2016
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings


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Overview

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.

Full Product Details

Author:   Joe Suzuki ,  Maomi Ueno
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   9505
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   4.336kg
ISBN:  

9783319283784


ISBN 10:   3319283782
Pages:   265
Publication Date:   25 February 2016
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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.

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