Symbolic and quantitative approaches to reasoning with uncertainty [electronic resource] : 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings / [edited by] Sébastien Destercke, Thierry Denoeux.
2015
Q334 .E87 2015eb
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Title
Symbolic and quantitative approaches to reasoning with uncertainty [electronic resource] : 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings / [edited by] Sébastien Destercke, Thierry Denoeux.
ISBN
9783319208077 electronic book
3319208071 electronic book
9783319208060
3319208071 electronic book
9783319208060
Published
[Cham] : Springer, 2015.
Language
English
Description
1 online resource (xiii, 554 pages) : illustrations.
Item Number
10.1007/978-3-319-20807-7 doi
Call Number
Q334 .E87 2015eb
Dewey Decimal Classification
003/.54
Summary
This book constitutes the refereed proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015, held in Compiègne, France, in July 2015. The 49 revised full papers presented were carefully reviewed and selected from 69 submissions and cover topics on decision theory and preferences; argumentation; conditionals; game theory; belief update; classification; inconsistency; graphical models; Bayesian networks; belief functions; logic; and probabilistic graphical models for scalable data analytics. Papers come from researchers interested in advancing the technology and from practitioners using uncertainty techniques in real-world applications. The scope of the ECSQARU conferences encompasses fundamental issues, representation, inference, learning, and decision making in qualitative and numeric uncertainty paradigms.
Note
International conference proceedings.
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Access limited to authorized users.
Series
Lecture notes in computer science ; 9161.
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Table of Contents
Decision theory and preferences
Argumentation.-Conditionals
Game theory
Belief update
Classification
Inconsistency
Graphical models
Bayesian Networks
Belief functions
Logic.-Probabilistic graphical models for scalable data analytics.
Argumentation.-Conditionals
Game theory
Belief update
Classification
Inconsistency
Graphical models
Bayesian Networks
Belief functions
Logic.-Probabilistic graphical models for scalable data analytics.