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Theorem that the binomial distribution approaches a normal distribution with [$ n \to \infty
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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!
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Later generalized to produce the central limit theorem
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I want to make sure I understand the flow of this binomial distribution whose limit is the normal distribution.
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For a random variable following a binomial distribution [$ B(n, p)
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For simplicity, let .
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We want to show that the following approximation holds when this is large
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Since it is troublesome if k is fixed while n grows, we put .
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That is,
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Using [Sterlingโs formula
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Stirlingโs formula is an approximation of factorials by powers
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Here comes the familiar !
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Proof from here
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- Eliminate factorials using Stirlingโs formula
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- Sort it out.
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About the first half
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The first half of this equation
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and
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to be the case.
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where is large because [$ k/n \approx p
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Now we have the first half of the equation we want to get.
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another solution
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reprint
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About the second half
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Second half of the equation
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and
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to be the case.
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Iโll try to form exp first.
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where , so
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Substituting
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log is approximated up to the second order by [Taylor expansion
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Use this.
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delete โkโ characters
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Expand each of the above terms, but ignore the third-order terms in x at this time
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Since these two add up to
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Thus, the second-order approximation shows that the following holds
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Separate solution (broke my heart in the process)
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Branching from here [$ \frac{\sqrt{n} }{ \sqrt{2\pi k (n-k)}}
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Erase k ,
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Put this expression as A. Take the logarithm of A
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- For ease of description, we define .
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where , which is due to Lagrangeโs mean value theorem, exists and
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Consider the possible range of absolute values of this value
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Note that x is minimal and , then .
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Note that x is at most , then
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Iโd like to say by showing that the absolute value converges to 0 as n increases, but I havenโt done it yet.
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ref
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