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Le Cam's theorem : ウィキペディア英語版
Le Cam's theorem
In probability theory, Le Cam's theorem, named after Lucien le Cam (1924 – 2000), states the following.
Suppose:
* ''X''1, ..., ''X''''n'' are independent random variables, each with a Bernoulli distribution (i.e., equal to either 0 or 1), not necessarily identically distributed.
* Pr(''X''''i'' = 1) = ''p''''i'' for ''i'' = 1, 2, 3, ...
* \lambda_n = p_1 + \cdots + p_n.\,
* S_n = X_1 + \cdots + X_n.\, (i.e. S_n follows a Poisson binomial distribution)
Then
:\sum_^\infty \left| \Pr(S_n=k) - \right| < 2 \sum_^n p_i^2.
In other words, the sum has approximately a Poisson distribution and the above inequality bounds the approximation error in terms of the total variation distance.
By setting ''p''''i'' = λ''n''/''n'', we see that this generalizes the usual Poisson limit theorem.
==References==

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抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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