Overview
- Presents the state of the art in probabilistic graphical models,
- Includes carefully edited and reviewed surveys and research articles
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 213)
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Table of contents (18 chapters)
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Front Matter
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Applications
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Front Matter
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Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Advances in Probabilistic Graphical Models
Editors: Peter Lucas, José A. Gámez, Antonio Salmerón
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-540-68996-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2007
Hardcover ISBN: 978-3-540-68994-2Published: 05 February 2007
Softcover ISBN: 978-3-642-08854-4Published: 19 November 2010
eBook ISBN: 978-3-540-68996-6Published: 12 June 2007
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: X, 386
Topics: Probability Theory and Stochastic Processes, Discrete Mathematics, Mathematical Modeling and Industrial Mathematics, Mathematical and Computational Engineering, Artificial Intelligence