Software for Data Analysis with Graphical Models

Wray L. Buntine, H. Scott Roy
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:76-86, 1995.

Abstract

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-buntine95a, title = {Software for Data Analysis with Graphical Models}, author = {Buntine, Wray L. and Roy, H. Scott}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {76--86}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/buntine95a/buntine95a.pdf}, url = {https://proceedings.mlr.press/r0/buntine95a.html}, abstract = {Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Software for Data Analysis with Graphical Models %A Wray L. Buntine %A H. Scott Roy %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-buntine95a %I PMLR %P 76--86 %U https://proceedings.mlr.press/r0/buntine95a.html %V R0 %X Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: 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. %Z Reissued by PMLR on 01 May 2022.
APA
Buntine, W.L. & Roy, H.S.. (1995). Software for Data Analysis with Graphical Models. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:76-86 Available from https://proceedings.mlr.press/r0/buntine95a.html. Reissued by PMLR on 01 May 2022.

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