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Lecture 10 Setup for the Central Limit Theorem 102 102 The Lindeberg Condition and Some Consequences We will write L(X) to denote the law or distribution of aSince Y 0, by Markov's inequality Pr(jX E(X)j E(Xuv) = Pr(fh 2 H h(u) = h(v)g = 1=n Xu;S = v6= u;v2SXuv, so E(Xu;S) = v6= u;v2SE(Xuv) jSj=n =N j=1 A j = m n j=1 A j = j=1 m(A j) = j=1 P(A j) which means that P is nitely additive We next show that P is continuous in ;(and hence

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