Learning in graphical models

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Learning in graphical models (2004)

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by Michael I. Jordan

Venue:	STATISTICAL SCIENCE
Citations:	806 - 10 self

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@ARTICLE{Jordan04learningin,
    author = {Michael I. Jordan},
    title = {Learning in graphical models},
    journal = {STATISTICAL SCIENCE},
    year = {2004},
    volume = {19},
    number = {1},
    pages = {140--155}
}

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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.

Keyphrases

graphical model general methodology language processing present example algorithmic idea complex way general graphical model formalism large-scale data analysis problem information retrieval statistical application random variable speech processing basic idea error-control coding image processing large-scale model

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