José A. Gámez – författare

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015, held in Albacete, Spain, in November 2015.

The 31 revised full papers presented were carefully selected from 175 submissions. The papers are organized in topical sections on Bayesian networks and uncertainty modeling; fuzzy logic and soft computing; knowledge representation, reasoning, and logic; intelligent systems and environment; intelligent Web and recommender systems; machine learning and data mining; metaheuristics and evolutionary computation; and social robotics.

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, held in Salamanca, Spain, in September 2016.

The 47 revised full papers presented were carefully selected from 166 submissions. Apart from the presentation of technical full papers, the scientific program of CAEPIA 2016 included an App contest, a Doctoral Consortium and, as a follow-up to the success achieved in previously CAEPIA editions, a special session on outstanding recent papers (Key Works) already published in renowned journals or forums.

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 considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;contributions to the area are coming from computer science, mathematics, statistics and engineering.

This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.