Nonparametric Estimation of Conditional Information and Divergences
Conference Paper, Proceedings of 15th International Conference on Artificial Intelligence and Statistics (AISTATS '12), pp. 914 - 923, April, 2012
Abstract
In this paper we propose new nonparametric estimators for a family of conditional
mutual information and divergences. Our estimators are easy to compute; they only use simple k nearest neighbor based statistics. We prove that the proposed conditional
information and divergence estimators are consistent under certain conditions, and demonstrate their consistency and applicability by numerical experiments on simulated and on real data as well.
BibTeX
@conference{Poczos-2012-119795,author = {B. Poczos and J. Schneider},
title = {Nonparametric Estimation of Conditional Information and Divergences},
booktitle = {Proceedings of 15th International Conference on Artificial Intelligence and Statistics (AISTATS '12)},
year = {2012},
month = {April},
pages = {914 - 923},
}
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