Learning in graphical models

Learning in graphical models (2004)

by Michael I. Jordan

Citations:

469 - 8 self

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Datum	Value	Source
TITLE	Learning in graphical models	INFERENCE
AUTHOR NAME	Michael I. Jordan	user correction
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. Key words and phrases: Probabilistic graphical models, junction tree algorithm, sum-product algorithm, Markov chain Monte Carlo, variational inference, bioinformatics, error-control coding.	user correction
YEAR	2004	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	140--155	user correction
VOLUME	19	INFERENCE
NUMBER	1	user correction
CITATIONS	40 found	ParsCit 1.0