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February 2004 Graphical Models
Michael I. Jordan
Statist. Sci. 19(1): 140-155 (February 2004). DOI: 10.1214/088342304000000026

Abstract

Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the models developed by researchers in these applied fields are instances of the general graphical model formalism. We review some of the basic ideas underlying graphical models, including the algorithmic ideas that allow graphical models to be deployed in large-scale data analysis problems. We also present examples of graphical models in bioinformatics, error-control coding and language processing.

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Michael I. Jordan. "Graphical Models." Statist. Sci. 19 (1) 140 - 155, February 2004. https://doi.org/10.1214/088342304000000026

Information

Published: February 2004
First available in Project Euclid: 14 July 2004

zbMATH: 1057.62001
MathSciNet: MR2082153
Digital Object Identifier: 10.1214/088342304000000026

Rights: Copyright © 2004 Institute of Mathematical Statistics

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Vol.19 • No. 1 • February 2004
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