[PDF.74wn] Graphical Models: Foundations of Neural Computation (Computational Neuroscience)

Graphical Models: Foundations of Neural Computation (Computational Neuroscience)

[PDF.ex21] Graphical Models: Foundations of Neural Computation (Computational Neuroscience)

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 | #834588 in Books |  2001-09-01 | Original language:English | PDF # 1 |  9.00 x1.00 x6.00l,1.31 | File type: PDF | 435 pages

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||About the Author||Michael 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.

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 ...

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