Author: Yang Xiang
Publisher: Cambridge University Press
Keywords: models, approach, graphical, systems, reasoning, multiagent, probabilistic
Number of Pages: 320
Published: 2002-09-15
List price: $85.00
ISBN-10: 0521813085
ISBN-13: 9780521813082

Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a

Authors:Daphne Koller, Nir Friedma,
Publisher: The MIT Pre
Keywords: computation, machine, learning, adaptive, techniques, graphical, models, principles, probabilistic
Number of Pages: 1208
Published: 2009-08-31
List price: $95.00
ISBN-10: 0262013193
ISBN-13: 9780262013192

Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications

Authors:Peter Lucas, José A. Gámez, Antonio Salmerón Cerdan,
Publisher: Springer
Keywords: fuzziness, soft, computing, studies, models, probabilistic, graphical, advances
Number of Pages: 396
Published: 2007-03-12
List price: $169.00
ISBN-10: 354068994X
ISBN-13: 9783540689942

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 chara

Authors:Antonino Freno, Edmondo Trentin,
Publisher: Springer
Keywords: models, graphical, intelligent, systems, library, reference, probabilistic, learning, fields, random, scalable, approach, parameter, structure, hybrid
Number of Pages: 228
Published: 2011-03-04
List price: $129.00
ISBN-10: 3642203078
ISBN-13: 9783642203077

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.-- Manfred Jaeger, Aalborg UniversitetThe book not only marks an e

Author: Steffen L. Lauritzen
Publisher: Oxford University Press, USA
Keywords: science, series, statistical, oxford, models, graphical
Number of Pages: 312
Published: 1996-07-25
List price: $135.00
ISBN-10: 0198522193
ISBN-13: 9780198522195

The application of graph theory to modelling systems began in several scientific areas, among them statistical physics (the study of large particle systems), genetics (studying inheritable properties of natural species), and interactions in contingency tables. The use of graphical models in statistics has increased considerably in these and other areas such as artificial intelligence, and the theory has been greatly developed and extended. This is the first comprehensive and authoritative account of the theory of graphical models. Written by a leading expert in the field, it contains the funda

Authors:Christian Borgelt, Rudolf Kruse,
Publisher: Wiley
Keywords: analysis, mining, data, methods, models, graphical
Number of Pages: 368
Published: 2002-03-15
List price: $150.00
ISBN-10: 0470843373
ISBN-13: 9780470843376

The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphic

Authors:Martin J Wainwright, Michael I Jordan,
Publisher: Now Publishers Inc
Keywords: variational, inference, families, exponential, models, graphical
Number of Pages: 324
Published: 2008-12-16
List price: $125.00
ISBN-10: 1601981848
ISBN-13: 9781601981844

The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances-including the key problems of computing marginals and modes of prob