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Learning in graphical models (2004)

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by Michael I. Jordan
Venue:STATISTICAL SCIENCE
Citations:803 - 10 self
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BibTeX

@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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