Lecture 11 : Clustering and the Spectral Partitioning Algorithm
نویسندگان
چکیده
In the design of randomized algorithms, we sometimes wish to estimate some quantity using an unbiased estimator. (Say we wish to estimate some quantity A, X ∼ μ is an unbiased estimator if E[X] = A.) We would like those estimations to be close to its mean w.h.p. Given some distribution μ, let Y1, · · · , Yn be i.i.d samples from μ and Y = 1 n ∑ i Yi be the empirical mean. Recall that in Problem 3, PS2, setting n = 25 or 50 doesn’t make that much of a difference.
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