Graphical Models: Foundations of Neural Computation

Description

This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. 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. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss

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Other editions

• 

  Graphical Models: Representations for Learning, Reasoning and Data Mining

  • 2009-07-30
  • 404 pages
  • Ebook
  • John Wiley & Sons
• 

  Graphical Models: Methods for Data Analysis and Mining

  • 2002-03-15
  • 368 pages
  • Paperback
  • Wiley
• 

  Graphical Models

  • 1996-05-02
  • 314 pages
  • Ebook
  • Clarendon Press

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