A kernel two-sample test
Published in Journal of Machine Learning Research • Mar 1, 2012
NobleIDNI5P67W68R52S16
Authors:,,
Arthur Gretton
Karsten Borgwardt
Malte J. Rasch
Abstract
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the largest difference in expectations over functions in the unit ball of a reproducing kernel Hilbe...
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