Uncertainty in Artificial Intelligence
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Discovering causal structures in binary exclusive-or skew acyclic models
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Abstract:
Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for binary data and propose a new approach to derive an identifiable causal structure governing the data based on skew Bernoulli distributions of external noise. Experimental evaluation shows excellent performance for both artificial and real world data sets.
Keywords:
Pages: 373-382
PS Link:
PDF Link: /papers/11/p373-inazumi.pdf
BibTex:
@INPROCEEDINGS{Inazumi11,
AUTHOR = "Takanori Inazumi and Takashi Washio and Shohei Shimizu and Joe Suzuki and Akihiro Yamamoto and Yoshinobu Kawahara",
TITLE = "Discovering causal structures in binary exclusive-or skew acyclic models",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "373--382"
}


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