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Math Help - Using the Central Limit Theorem to prove a limit

  1. #1
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    Using the Central Limit Theorem to prove a limit

    Use the central limit theorem to prove that, as M tends to infinite:

    exp(-2M) * SUM(exp(2Mn)/n!) --> 1/2 . The sum goes from n=0 to n=M.

    I don't know how to format it correctly, sorry.

    I sort of know what to do with this question. I need to find the right variables so the left side of the CLT boils down to what's above, and the right would be the normal distribution, presumably from 0 to infinite to get a half. A hint or a correction if I'm wrong will be brilliant to start me off, thanks

    James
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  2. #2
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    Quote Originally Posted by phillips101 View Post
    Use the central limit theorem to prove that, as M tends to infinite:

    exp(-2M) * SUM(exp(2Mn)/n!) --> 1/2 . The sum goes from n=0 to n=M.

    I don't know how to format it correctly, sorry.

    I sort of know what to do with this question. I need to find the right variables so the left side of the CLT boils down to what's above, and the right would be the normal distribution, presumably from 0 to infinite to get a half. A hint or a correction if I'm wrong will be brilliant to start me off, thanks

    James
    There's a mistake in your formula (the left-hand side is greater than 1, and it diverges), I think it should be:

    e^{-M}\sum_{n=0}^M \frac{M^k}{k!}\to_{M\to\infty} \frac{1}{2}

    Anyway, your intuition is correct ; consider applying the CLT to Poisson random variables of parameter 1 (remember the sum of n independent such variables is Poisson distributed with parameter n).
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  3. #3
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    Quote Originally Posted by Laurent View Post
    There's a mistake in your formula (the left-hand side is greater than 1, and it diverges), I think it should be:

    e^{-M}\sum_{n=0}^M \frac{M^k}{k!}\to_{M\to\infty} \frac{1}{2}

    Anyway, your intuition is correct ; consider applying the CLT to Poisson random variables of parameter 1 (remember the sum of n independent such variables is Poisson distributed with parameter n).
    That's what I thought! I really didn't think it would converge, and none of the standard distributions gave the right answer.

    Thanks for the confirmation. I'll inquire about the error.
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