9780262600422

Graphical Models : Foundations of Neural Computation

by
  • ISBN13:

    9780262600422

  • ISBN10:

    0262600420

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2001-09-01
  • Publisher: MIT PRESS
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Supplemental Materials

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  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

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. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss.

Table of Contents

Series Foreword vii
Sources ix
Introduction xi
Probabilistic Independence Networks for Hidden Markov Probability Models
1(44)
Padhraic Smyth
David Heckerman
Micheal I. Jordan
Learning and Relearning in Boltzmann Machines
45(32)
G. E. Hinton
T. J. Sejnowski
Learning in Boltzmann Trees
77(12)
Lawrence Saul
Michael I. Jordan
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space
89(8)
Geoffrey E. Hinton
Attractor Dynamics in Feedforward Neural Networks
97(24)
Lawrence K. Saul
Michael I. Jordan
Efficient Learning in Boltzmann Machines Using Linear Response Theory
121(20)
H. J. Kappen
F. B. Rodriguez
Asymmetric Parallel Boltzmann Machines Are Belief Networks
141(4)
Radford M. Neal
Variational Learning in Nonlinear Gaussian Belief Networks
145(22)
Brendan J. Frey
Geoffrey E. Hinton
Mixtures of Probabilistic Principal Component Analyzers
167(40)
Michael E. Tipping
Christopher M. Bishop
Independent Factor Analysis
207(50)
H. Attias
Hierarchical Mixtures of Experts and the EM Algorithm
257(34)
Michael I. Jordan
Robert A. Jacobs
Hidden Neural Networks
291(24)
Anders Krogh
Soren Kamaric Riis
Variational Learning for Switching State-Space Models
315(34)
Zoubin Ghahramani
Geoffrey E. Hinton
Nonlinear Time-Series Prediction with Missing and Noisy Data
349(18)
Volker Tresp
Reimar Hofmann
Correctness of Local Probability Propagation in Graphical Models with Loops
367(42)
Yair Weiss
Index 409

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