A characterization of distributions by mean values of statistics and certain probabilistic metrics

By A. Zinger, A. Kakosyan, L. Klebanov. 1992

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Fast Two-Sample Testing with Analytic Representations of Probability Measures (2015, background): “…distance (sup-norm between cumulative distribution functions); the Maximum Mean Discrepancy (MMD) [8], an RKHS norm of the difference between mean embeddings, and the N -distance (also known as energy distance) [33, 30, 3], which is an MMD-based test for a particular family of kernels [24] .”
Geometrical Insights for Implicit Generative Modeling (2017, background): “ity (2.iii) comes for free with this condition (Corollary 2.19). The second part of the theorem gives the necessary and sufﬁcient condition for satisfying the separation property (2.i). Theorem 2.12 ([57]). The right-hand side of deﬁnition (17) is: i)nonnegative for all P;Qin P1 X if and only if the”
Chapter 21 - Aspects of the Isometric Theory of Banach Spaces (2001, unknown): “Applications and generalizations of the extension method also include [2], [18] (see Section 3), [30], [41], [42], [52], [55], [78], [79], [87], [85], [86], [89], [95], [113], [116], [122], [124], [134].”