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About this product
- DescriptionGraphical 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 vel 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. Rodriguez, L. K. Saul, Terrence J. Sejwski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.
- Author BiographyMichael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley, and recipient of the ACM/AAAI Allen Newell Award.
- PublisherMIT Press Ltd
- Date of Publication03/12/2001
- SubjectComputing: Professional & Programming
- Series TitleComputational Neuroscience
- Place of PublicationMassachusetts
- Country of PublicationUnited States
- ImprintBradford Books
- Content NoteIllustrations
- Weight599 g
- Width152 mm
- Height229 mm
- Spine25 mm
- Edited byMichael I. Jordan,Terrence J. Sejnowski
- Format DetailsTrade paperback (US)
- Interest AgeFrom 18
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