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- W2951341231 abstract "We investigate the problem of testing the equivalence between two discrete histograms. A {em $k$-histogram} over $[n]$ is a probability distribution that is piecewise constant over some set of $k$ intervals over $[n]$. Histograms have been extensively studied in computer science and statistics. Given a set of samples from two $k$-histogram distributions $p, q$ over $[n]$, we want to distinguish (with high probability) between the cases that $p = q$ and $|p-q|_1 geq epsilon$. The main contribution of this paper is a new algorithm for this testing problem and a nearly matching information-theoretic lower bound. Specifically, the sample complexity of our algorithm matches our lower bound up to a logarithmic factor, improving on previous work by polynomial factors in the relevant parameters. Our algorithmic approach applies in a more general setting and yields improved sample upper bounds for testing closeness of other structured distributions as well." @default.
- W2951341231 created "2019-06-27" @default.
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- W2951341231 date "2017-03-06" @default.
- W2951341231 modified "2023-09-27" @default.
- W2951341231 title "Near-Optimal Closeness Testing of Discrete Histogram Distributions" @default.
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