Kernel mean embedding of distributions: A review and beyond
A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
recently emerged as a powerful tool for machine learning and statistical inference. The basic …
A review of multivariate distributions for count data derived from the Poisson distribution
The Poisson distribution has been widely studied and used for modeling univariate count‐
valued data. However, multivariate generalizations of the Poisson distribution that permit …
valued data. However, multivariate generalizations of the Poisson distribution that permit …
Central moment discrepancy (cmd) for domain-invariant representation learning
W Zellinger, T Grubinger, E Lughofer… - ar**
In digital soil map** (DSM), soil maps are usually produced in a univariate manner, that is,
each soil map is produced independently and therefore, when multiple soil properties are …
each soil map is produced independently and therefore, when multiple soil properties are …
Efficient Aggregated Kernel Tests using Incomplete -statistics
We propose a series of computationally efficient, nonparametric tests for the two-sample,
independence and goodness-of-fit problems, using the Maximum Mean Discrepancy …
independence and goodness-of-fit problems, using the Maximum Mean Discrepancy …
Fast two-sample testing with analytic representations of probability measures
KP Chwialkowski, A Ramdas… - Advances in Neural …, 2015 - proceedings.neurips.cc
We propose a class of nonparametric two-sample tests with a cost linear in the sample size.
Two tests are given, both based on an ensemble of distances between analytic functions …
Two tests are given, both based on an ensemble of distances between analytic functions …