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The discovery of generalized causal models with mixed variables using MML criterion

conference contribution
posted on 2004-01-01, 00:00 authored by Gang LiGang Li, Honghua Dai
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.

History

Title of proceedings

Proceedings of the Fourth SIAM International Conference on Data Mining

Event

SIAM International Conference on Data Mining (4th: 2004: Lake Buena Vista, Fla.)

Pagination

487 - 491

Publisher

Society for Industrial and Applied Mathematics

Location

Lake Buena Vista, Fla.

Place of publication

Philadelphia, Pa.

Start date

2004-04-22

End date

2004-04-24

ISBN-13

9780898715682

ISBN-10

0898715687

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

M Berry, U Dayal, C Kamath, D Skillicorn

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