A Kernel Two-Sample Test
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 Hilbert space (RKHS), and is called the maximum mean discrepancy (MMD).We present two distributionfree tests based on large deviation bounds for the MMD, and a third test based on the asymptotic...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|01_Gretton_A_Kernel_Two-Sample_Test_2012.pdf||460.92 kB||Adobe PDF||Request a copy|
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