Erich P. Stuntebeck, John S. Davis II, et al.
HotMobile 2008
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. © 2013 ACM.
Erich P. Stuntebeck, John S. Davis II, et al.
HotMobile 2008
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VTS 1998
Raymond Wu, Jie Lu
ITA Conference 2007
Ehud Altman, Kenneth R. Brown, et al.
PRX Quantum