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Advances in Bayesian Networks
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.
Advances in Probabilistic Graphical Models
This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.
Learning and Reasoning in Hybrid Structured Spaces
Artificial intelligence often has to deal with uncertain scenarios, such as a partially observed environment or noisy observations. Traditional probabilistic models, while being very principled approaches in these contexts, are incapable of dealing with both algebraic and logical constraints. Existing hybrid continuous/discrete models are typically limited in expressivity, or do not offer any guarantee on the approximation errors. This book, Learning and Reasoning in Hybrid Structured Spaces, discusses a recent and general formalism called Weighted Model Integration (WMI), which enables probabilistic modeling and inference in hybrid structured domains. WMI-based inference algorithms differ w...
Advances in Intelligent Data Analysis XI
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Analysis, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 88 submissions. All current aspects of intelligent data analysis are addressed, including intelligent support for modeling and analyzing data from complex, dynamical systems. The papers focus on novel applications of IDA techniques to, e.g., networked digital information systems; novel modes of data acquisition and the associated issues; robustness and scalability issues of intelligent data analysis techniques; and visualization and dissemination results.
Advances in Intelligent Data Analysis XII
This book constitutes the refereed conference proceedings of the 12th International Conference on Intelligent Data Analysis, which was held in October 2013 in London, UK. The 36 revised full papers together with 3 invited papers were carefully reviewed and selected from 84 submissions handling all kinds of modeling and analysis methods, irrespective of discipline. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems.
Análisis de indicadores de rendimiento mediante redes bayesianas
Los indicadores de rendimiento han cobrado una especial relevancia como soporte de los diversos mecanismos de mejora de la calidad. En Educación Superior, la aplicación de modelos de financiación basados en la consecución de objetivos ha situado a los indicadores de rendimiento en el punto de mira de los gestores de las universidades públicas: su análisis es de vital importancia para la toma de decisiones, la administración y la política de planificación universitaria. En este libro hemos pretendido acercarnos al estudio de los indicadores de rendimiento en el ámbito de la Educación Superior proponiendo una metodología de análisis basada en el uso de redes bayesianas, que ofrece...
Manual básico de Estadística para Ingeniería Informática
Actualmente existen numerosos libros de texto que cubren los contenidos de la asignatura básica de Estadística correspondiente a las titulaciones de ingeniería. Sin embargo, son pocos los textos que están especialmente diseñados para la ingeniería informática. Por un lado, los textos genéricos de estadística para ingeniería pueden resultar poco atractivos para los estudiantes de informática, pues carecen de ejemplos propios de su actividad profesional, por lo que resulta difícil situar el contexto de la estadística dentro de la profesión de ingeniería informática. Por otro lado, los textos específicos de estadística para informática son en su mayoría extranjeros y no se c...
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.
