A kernelized Stein discrepancy for goodness-of-fit tests

Qiang Liu, Jason D. Lee, Michael Jordan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

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.

Original languageEnglish (US)
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsMaria Florina Balcan, Kilian Q. Weinberger
PublisherInternational Machine Learning Society (IMLS)
Pages448-461
Number of pages14
ISBN (Electronic)9781510829008
StatePublished - Jan 1 2016
Externally publishedYes
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Publication series

Name33rd International Conference on Machine Learning, ICML 2016
Volume1

Other

Other33rd International Conference on Machine Learning, ICML 2016
CountryUnited States
CityNew York City
Period6/19/166/24/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

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  • Cite this

    Liu, Q., Lee, J. D., & Jordan, M. (2016). A kernelized Stein discrepancy for goodness-of-fit tests. In M. F. Balcan, & K. Q. Weinberger (Eds.), 33rd International Conference on Machine Learning, ICML 2016 (pp. 448-461). (33rd International Conference on Machine Learning, ICML 2016; Vol. 1). International Machine Learning Society (IMLS).