Graphical models : foundations of neural computation / edited by Michael I. Jordan and Terrence J. Sejnowski.
Contributor(s): Jordan, Michael Irwin
| Sejnowski, Terrence J. (Terrence Joseph) | IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Computational neuroscience: Publisher: Cambridge, Massachusetts : MIT Press, c2001Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2001]Description: 1 PDF (xxiv, 421 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262291200.Subject(s): Neural networks (Computer science) | Computer graphicsGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3/2 Online resources: Abstract with links to resource Also available in print.Summary: Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodr�iguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.
"A Bradford book."
Includes bibliographical references and index.
Restricted to subscribers or individual electronic text purchasers.
Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodr�iguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.
Also available in print.
Mode of access: World Wide Web
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