Description - Learning in Graphical Models by Michael I. Jordan
Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering - uncertainty and complexity. This book presents an exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters: inference for Bayesian networks; Monte Carlo methods; variational methods; and learning with Bayesian networks. The remaining chapters cover a range of topics.
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(254mm x 178mm x 32mm)
Publisher: MIT Press Ltd
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Author Biography - Michael I. Jordan
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.