Symbolic and Quantitative Approaches to Reasoning with Uncertainty

This book constitutes the refereed proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2015, held in Compiegne, 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.

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.

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.