Advanced Methodologies for Bayesian Networks : Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings / edited by Joe Suzuki, Maomi Ueno.

Format:

Book

Contributor:

Suzuki, Joe, editor.

Ueno, Maomi, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 9505.

Lecture Notes in Artificial Intelligence ; 9505

Language:

English

Subjects (All):

Artificial intelligence.

Algorithms.

Mathematical statistics.

Computers.

Database management.

Application software.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Probability and Statistics in Computer Science.

Computation by Abstract Devices.

Database Management.

Information Systems Applications (incl. Internet).

Local Subjects:

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Probability and Statistics in Computer Science.

Computation by Abstract Devices.

Database Management.

Information Systems Applications (incl. Internet).

Physical Description:

1 online resource (XVIII, 265 pages) : 102 illustrations in color.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-28379-1

9783319283791

Access Restriction:

Restricted for use by site license.