The Central Limit Theorem Mathematical Proof

The central limit theorem says that the sample means of a any probability distribution with the sample size large enough, the sample means follow a normal distribution. This is useful in a way that, in real life, we don't certainly know what distribution things follow, and we may use the central limit theorem to model real life random events as long as we take big enough samples.

Moment Generating Function(MGF)

Moment generating function is a way to describe a probability distribution just like a probability density function(PDF) or a cumulative distribution function(CDF) does.

It is intuitive that two probability distributions are the same if their PDFs are the same. 

What if their MGFs are the same? 

Definition

An MGF is defined as Mx(t) = E[etx], and if we write out its Taylor series expansion, we get 
Therefore,
If we plug 0 into t, we get Mx(0) = E[e0] = 1.
If we take the first derivative of Mx(t) with respect to t, we get
Then if we plug 0 into t, we get E[x].

So, we can get the nth moment of a probability distribution by taking its nth derivative with respect to t and plug 0 into t.

Uniqueness

If two probability distributions have the same MGFs, they have same moments of all order. 

For example. two exact probability distributions have the same mean and the same variance, mean being E[x] and the variance being E[x2] - E[x]2, and their first and second moments must be the same.

On the other hand, if two probability distributions are not the same, there must exist a moment of order n being different.

Central limit theorem proof

To proof the central limit theorem, we need one property of MGF.
If x and y are independent:
Mx+y(t) = E[et(x+y)] = E[etxety] = E[etx] E[ety] = Mx(t) My(t) 

Proof

Suppose we take samples of size n form a probability distribution and  treat the sample means as random variable, we can prove that the sample means have a MGF of a normal distribution.

Let a random sample be x1 + x2 + x3 +...+ xn and its mean be S
         S = (x1 + x2 + x3 +...+ xn) / n

The mathematical derivation is as the following:

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