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Poster
Nearly-Tight Bounds for Testing Histogram Distributions
Clément L Canonne · Ilias Diakonikolas · Daniel Kane · Sihan Liu

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #829
in Poster Session 5 »
We investigate the problem of testing whether a discrete probability distribution over an ordered domain is a histogram on a specified number of bins. One of the most common tools for the succinct approximation of data, $k$-histograms over $[n]$, are probability distributions that are piecewise constant over a set of $k$ intervals. Given samples from an unknown distribution $\mathbf p$ on $[n]$, we want to distinguish between the cases that $\mathbf p$ is a $k$-histogram versus far from any $k$-histogram, in total variation distance. Our main result is a sample near-optimal and computationally efficient algorithm for this testing problem, and a nearly-matching (within logarithmic factors) sample complexity lower bound, showing that the testing problem has sample complexity $\widetilde \Theta (\sqrt{nk} / \epsilon + k / \epsilon^2 + \sqrt{n} / \epsilon^2)$.
Keywords: lower bounds · binning · probability distributions · distribution testing · histograms · sub-linear algorithms

Author Information

Clément L Canonne (IBM Research)
Ilias Diakonikolas (University of Wisconsin-Madison)
Daniel Kane (UCSD)
Sihan Liu (Computer Science and Engineering Department, University of California, San Diego)

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