Software for Data Analysis with Graphical Models
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
Text
Buntine and Roy. "Software for Data Analysis with Graphical Models." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Buntine and Roy. "Software for Data Analysis with Graphical Models." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/buntine1995aistats-software/)BibTeX
@inproceedings{buntine1995aistats-software,
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},
year = {1995},
pages = {76-86},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/buntine1995aistats-software/}
}