Artikel,

Testing Identity of Structured Distributions

, , und .
(2014)cite arxiv:1410.2266Comment: 21 pages, to appear in SODA'15.

Zusammenfassung

We study the question of identity testing for structured distributions. More precisely, given samples from a structured distribution $q$ over $n$ and an explicit distribution $p$ over $n$, we wish to distinguish whether $q=p$ versus $q$ is at least $\epsilon$-far from $p$, in $L_1$ distance. In this work, we present a unified approach that yields new, simple testers, with sample complexity that is information-theoretically optimal, for broad classes of structured distributions, including $t$-flat distributions, $t$-modal distributions, log-concave distributions, monotone hazard rate (MHR) distributions, and mixtures thereof.

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