The discovery of generalized causal models with mixed variables using MML criterion
Contribuinte(s) |
Berry, Michael Dayal, Umeshwar Kamath, Chandrika Skillicorn, David |
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Data(s) |
01/01/2004
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Resumo |
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.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Society for Industrial and Applied Mathematics |
Relação |
http://dro.deakin.edu.au/eserv/DU:30005416/dai-thediscoveryofgeneralized-2004.pdf http://www.siam.org/proceedings/datamining/2004/dm04_052lig.pdf |
Tipo |
Conference Paper |