Stronger xi can follow any distribution with nite

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Unformatted text preview: s for this upper bound to be less than δ . According to this sufficient condition, we set n large enough so that 1 ≤δ 4nε 2 Plugging in δ = 0.01 and ε = 0.05, we obtain nmin = 10000. M. Chen (IE@CUHK) ENGG2430C lecture 10 22 / 29 Beyond Convergence in Probability 1 The Law of Large Number says that the sample mean Yn = n ∑n=1 Xi i is very likely to be close to the true mean E [X ] as n goes large. Xi can follow any distribution, with finite mean. The Central Limit Theorem further says that Yn approximately follows Gaussian distribution as n goes large. (Stronger) Xi can follow any distribution, with finite mean and variance. M. Chen (IE@CUHK) ENGG2430C lecture 10 23 / 29 Central Limit Theorem (CLT) Let X1 , X2 , . . . be an infinite sequence of independent identically distributed r.v.s with mean µ and variance σ 2 . Define a r.v. Zn = X1 + X2 + · · · + Xn − n µ √ . σn Then the CDF of Zn converges to the standard normal CDF N (0, 1): ˆz 2 1 Φ(z ) = √ e −x /2 dx , 2π −∞ in the sense that lim P (Zn ≤ z ) = Φ(z ), n→∞ for every z . Xi can follow any distributi...
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This document was uploaded on 03/31/2014.

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