• Theorem that the binomial distribution approaches a normal distribution with [$ n \to \infty

  • Discovered by de moivre in 1733.

    • At that time, the term normal distribution did not yet exist.
    • Gauss was born in 1777.
    • de moivre amazing!
  • Later generalized to produce the central limit theorem

  • I want to make sure I understand the flow of this binomial distribution whose limit is the normal distribution.

  • For a random variable following a binomial distribution [$ B(n, p)

  • For simplicity, let .

  • We want to show that the following approximation holds when this is large

  • Since it is troublesome if k is fixed while n grows, we put .

    • That is,

    • Using [Sterlingโ€™s formula

    • Stirlingโ€™s formula is an approximation of factorials by powers

    • Here comes the familiar !

Proof from here

    • Eliminate factorials using Stirlingโ€™s formula
    • Sort it out.
    • About the first half

      • The first half of this equation

      • and

      • to be the case.

      • where is large because [$ k/n \approx p

      • Now we have the first half of the equation we want to get.

      • another solution

    • reprint

    • About the second half

      • Second half of the equation

      • and

      • to be the case.

      • Iโ€™ll try to form exp first.

      • where , so

      • Substituting

      • log is approximated up to the second order by [Taylor expansion

      • Use this.

      • delete โ€œkโ€ characters

      • Expand each of the above terms, but ignore the third-order terms in x at this time

      • Since these two add up to

    • Thus, the second-order approximation shows that the following holds

Separate solution (broke my heart in the process)

  • Branching from here [$ \frac{\sqrt{n} }{ \sqrt{2\pi k (n-k)}}

    • Erase k ,

    • Put this expression as A. Take the logarithm of A

      • For ease of description, we define .
    • where , which is due to Lagrangeโ€™s mean value theorem, exists and

    • Consider the possible range of absolute values of this value

    • Note that x is minimal and , then .

    • Note that x is at most , then

    • Iโ€™d like to say by showing that the absolute value converges to 0 as n increases, but I havenโ€™t done it yet.

  • ref


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