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DOI: 10.4230/LIPIcs.STACS.2017.31
URN: urn:nbn:de:0030-drops-70024
URL: http://drops.dagstuhl.de/opus/volltexte/2017/7002/
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Fischer, Eldar ; Lachish, Oded ; Vasudev, Yadu

Improving and Extending the Testing of Distributions for Shape-Restricted Properties

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Abstract

Distribution testing deals with what information can be deduced about an unknown distribution over {1,...,n}, where the algorithm is only allowed to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allowed to obtain samples from the restriction of the original distribution on subsets of {1,...,n}. In 2015, Canonne, Diakonikolas, Gouleakis and Rubinfeld unified several previous results, and showed that for any property of distributions satisfying a "decomposability" criterion, there exists an algorithm (in the basic model) that can distinguish with high probability distributions satisfying the property from distributions that are far from it in variation distance. We present here a more efficient yet simpler algorithm for the basic model, as well as very efficient algorithms for the conditional model, which until now was not investigated under the umbrella of decomposable properties. Additionally, we provide an algorithm for the conditional model that handles a much larger class of properties. Our core mechanism is a way of efficiently producing an interval-partition of {1,...,n} that satisfies a "fine-grain" quality. We show that with such a partition at hand we can directly move forward with testing individual intervals, instead of first searching for the "correct" partition of {1,...,n}.

BibTeX - Entry

@InProceedings{fischer_et_al:LIPIcs:2017:7002,
  author =	{Eldar Fischer and Oded Lachish and Yadu Vasudev},
  title =	{{Improving and Extending the Testing of Distributions for Shape-Restricted Properties}},
  booktitle =	{34th Symposium on Theoretical Aspects of Computer Science (STACS 2017)},
  pages =	{31:1--31:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-028-6},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{66},
  editor =	{Heribert Vollmer and Brigitte Vallée},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7002},
  URN =		{urn:nbn:de:0030-drops-70024},
  doi =		{10.4230/LIPIcs.STACS.2017.31},
  annote =	{Keywords: conditional sampling, distribution testing, property testing, statistics}
}

Keywords: conditional sampling, distribution testing, property testing, statistics
Seminar: 34th Symposium on Theoretical Aspects of Computer Science (STACS 2017)
Issue Date: 2017
Date of publication: 24.02.2017


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