Serafín Moral - Böcker

This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017.

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.

In recent years it has become apparent that an importantpart of the theory of artificial intelligence is concernedwith reasoning on the basis of uncertain, incomplete, orinconsistent information. A variety of formalisms have beendeveloped, including nonmonotonic logic, fuzzy sets,possibility theory, belief functions, and dynamic models ofreasoning such as belief revision and Bayesian networks. Several European research projects have been formed in thearea and the first European conference was held in 1991. This volume contains the papers accepted for presentation atECSQARU-93, the European Conference on SymbolicandQuantitative Approaches to Reasoning and Uncertainty, heldat the University of Granada, Spain, November 8-10, 1993.

In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.