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The discovery of generalized causal models with mixed variables using MML criterion
One major difficulty frustrating the application of linear causal models is that they are not easily adapted to cope with discrete data. This is unfortunate since most real problems involve both continuous and discrete variables. In this paper, we consider a class of graphical models which allow both continuous and discrete variables, and propose the parameter estimation method and a structure discovery algorithm based on Minimum Message Length and parameter estimation. Experimental results are given to demonstrate the potential for the application of this method.
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Title of proceedings
Proceedings of the Fourth SIAM International Conference on Data MiningEvent
SIAM International Conference on Data Mining (4th: 2004: Lake Buena Vista, Fla.)Pagination
487 - 491Publisher
Society for Industrial and Applied MathematicsLocation
Lake Buena Vista, Fla.Place of publication
Philadelphia, Pa.Start date
2004-04-22End date
2004-04-24ISBN-13
9780898715682ISBN-10
0898715687Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
M Berry, U Dayal, C Kamath, D SkillicornUsage metrics
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