Concentration Inequalities - UPF
fX tg X t: If follows from Markov’s inequality that if ˚is a strictly monotonically increasing nonnegative-valued function then for any random variable Xand real number t, fX tg= f˚(X) ˚(t)g ˚(X) ˚(t): An application of this with ˚(x) = x2 is Chebyshev’s inequality: if X is an arbitrary random variable and t>0, then fjX Xj tg= jX Xj2 ...
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