Overview

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 title seeks to exemplify 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.

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9780262600422

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Michael I. Jordan is Professor of Electrical Engineering and Computer Science and of Statistics at the University of California, Berkeley. He is the editor of Learning in Graphical Models (MIT Press, 1999), Terrence J. Sejnowski is Head of the Department of Computational Neurobiology at the Salk Institute of Biological Studies and Professor of Biology at the University of California, San Diego. He is the coeditor of Unsupervised Learning and Map Formation (MIT Press, 1999) and of Neural Codes and Distributed Representation (MIT Press, 1999) and the Editor-in-Chief of the journal Neural Computation.

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