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

Li, Gang and Dai, Honghua 2004, The discovery of generalized causal models with mixed variables using MML criterion, in Proceedings of the Fourth SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, Pa., pp. 487-491.

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Title The discovery of generalized causal models with mixed variables using MML criterion
Author(s) Li, Gang
Dai, Honghua
Conference name SIAM International Conference on Data Mining (4th: 2004: Lake Buena Vista, Fla.)
Conference location Lake Buena Vista, Fla.
Conference dates 22-24 April 2004
Title of proceedings Proceedings of the Fourth SIAM International Conference on Data Mining
Editor(s) Berry, Michael
Dayal, Umeshwar
Kamath, Chandrika
Skillicorn, David
Publication date 2004
Start page 487
End page 491
Publisher Society for Industrial and Applied Mathematics
Place of publication Philadelphia, Pa.
Summary 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.
ISBN 0898715687
9780898715682
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005416

Document type: Conference Paper
Collection: School of Information Technology
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