The Lindeberg condition is a criterion used in probability theory to determine whether the Central Limit Theorem applies to a sequence of random variables. It states that for a collection of independent random variables, if the contributions of each variable to the overall variance do not become excessively large as the number of variables increases, then the sum of these variables will converge in distribution to a normal distribution. This condition helps extend the applicability of the Central Limit Theorem beyond the cases of identical distribution, focusing on how individual random variables impact the limit.
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