Graphical Models : Foundations of Neural Computation

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  • Edition: 1st
  • Format: Paperback
  • Copyright: 2001-09-01
  • Publisher: MIT PRESS
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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
Padhraic Smyth
David Heckerman
Micheal I. Jordan
Learning and Relearning in Boltzmann Machines
G. E. Hinton
T. J. Sejnowski
Learning in Boltzmann Trees
Lawrence Saul
Michael I. Jordan
Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space
Geoffrey E. Hinton
Attractor Dynamics in Feedforward Neural Networks
Lawrence K. Saul
Michael I. Jordan
Efficient Learning in Boltzmann Machines Using Linear Response Theory
H. J. Kappen
F. B. Rodriguez
Asymmetric Parallel Boltzmann Machines Are Belief Networks
Radford M. Neal
Variational Learning in Nonlinear Gaussian Belief Networks
Brendan J. Frey
Geoffrey E. Hinton
Mixtures of Probabilistic Principal Component Analyzers
Michael E. Tipping
Christopher M. Bishop
Independent Factor Analysis
H. Attias
Hierarchical Mixtures of Experts and the EM Algorithm
Michael I. Jordan
Robert A. Jacobs
Hidden Neural Networks
Anders Krogh
Soren Kamaric Riis
Variational Learning for Switching State-Space Models
Zoubin Ghahramani
Geoffrey E. Hinton
Nonlinear Time-Series Prediction with Missing and Noisy Data
Volker Tresp
Reimar Hofmann
Correctness of Local Probability Propagation in Graphical Models with Loops
Yair Weiss
Index 409

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