@inproceedings{4162ecac9c7b4f2a8b77698b35660677,
title = "On the power of conditional samples in distribution testing",
abstract = "In this paper we define and examine the power of the conditional sampling oracle in the context of distribution-property testing. The conditional sampling oracle for a discrete distribution μ takes as input a subset S ⊂ [n] of the domain, and outputs a random sample i ∈ S drawn according to μ, conditioned on S (and independently of all prior samples). The conditional-sampling oracle is a natural generalization of the ordinary sampling oracle in which S always equals [n]. We show that with the conditional-sampling oracle, testing uniformity, testing identity to a known distribution, and testing any label-invariant property of distributions is easier than with the ordinary sampling oracle. On the other hand, we also show that for some distribution properties the sample complexity remains near-maximal even with conditional sampling.",
keywords = "conditional samples, distribution testing, statistical approximation",
author = "Sourav Chakraborty and Eldar Fischer and Yonatan Goldhirsh and Arie Matsliah",
year = "2013",
doi = "https://doi.org/10.1145/2422436.2422497",
language = "الإنجليزيّة",
isbn = "9781450318594",
series = "ITCS 2013 - Proceedings of the 2013 ACM Conference on Innovations in Theoretical Computer Science",
pages = "561--580",
booktitle = "ITCS 2013 - Proceedings of the 2013 ACM Conference on Innovations in Theoretical Computer Science",
note = "2013 4th ACM Conference on Innovations in Theoretical Computer Science, ITCS 2013 ; Conference date: 09-01-2013 Through 12-01-2013",
}