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Data Analysis with Graphical Models: Software ToolsProbabilistic graphical models (directed and undirected Markov fields, and combined in chain graphs) are used widely in expert systems, image processing and other areas as a framework for representing and reasoning with probabilities. They come with corresponding algorithms for performing probabilistic inference. This paper discusses an extension to these models by Spiegelhalter and Gilks, plates, used to graphically model the notion of a sample. This offers a graphical specification language for representing data analysis problems. When combined with general methods for statistical inference, this also offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper outlines the framework and then presents some basic tools for the task: a graphical version of the Pitman-Koopman Theorem for the exponential family, problem decomposition, and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.
Document ID
19990111710
Acquisition Source
Ames Research Center
Document Type
Reprint (Version printed in journal)
Authors
Buntine, Wray L.
(Research Inst. for Advanced Computer Science Moffett Field, CA United States)
Date Acquired
August 19, 2013
Publication Date
July 1, 1994
Subject Category
Computer Programming And Software
Report/Patent Number
FIA-94-10
Meeting Information
Meeting: Interface 1994
Location: Research Triangle Park, NC
Country: United States
Start Date: June 1, 1994
Distribution Limits
Public
Copyright
Other

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