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Statistical Inference with Limited Memory

Ofer Shayevitz, Tel Aviv University
E18-304

Abstract:  In statistical inference problems, we are typically given a limited number of samples from some underlying distribution, and we wish to estimate some property of that distribution, under a given measure of risk. We are usually interested in characterizing and achieving the best possible risk as a function of the number of available samples. Thus, it is often implicitly assumed that samples are co-located, and that communication bandwidth as well as computational power are not a bottleneck, essentially making the number…

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