Learning discrete probability distributions with a multi-resolution binary tree

Authors:
F. A. Sanchís;F. Aznar;M. Sempere;M. Pujol;R. Rizo
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante;Department of Computer Science and Artificial Intelligence, University of Alicante
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

In this paper a method for learning and representing joint probabilistic distributions, using binary trees, is shown. This method could be used with the Bayesian Programming formalism, being a very useful tool when working with real world data. It has the advantage of learning unknown probabilistic distributions directly from raw data, and to remain more balanced than other previous methods. Finally, an application to learn a fuzzy control system, using this approach, will be presented.