@inproceedings{8d62a226276a4c018fa050503e8cb171,
title = "A kernelized Stein discrepancy for goodness-of-fit tests",
abstract = "We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-of-fit tests that are widely applicable for complex and high dimensional distributions, even for those with computationally intractable normalization constants. Both theoretical and empirical properties of our methods are studied thoroughly.",
author = "Qiang Liu and Lee, {Jason D.} and Michael Jordan",
year = "2016",
language = "English (US)",
series = "33rd International Conference on Machine Learning, ICML 2016",
publisher = "International Machine Learning Society (IMLS)",
pages = "448--461",
editor = "Balcan, {Maria Florina} and Weinberger, {Kilian Q.}",
booktitle = "33rd International Conference on Machine Learning, ICML 2016",
note = "33rd International Conference on Machine Learning, ICML 2016 ; Conference date: 19-06-2016 Through 24-06-2016",
}