human
Write a proof of the central limit theorem
gpt
The central limit theorem, also known as the central limit theorem of probability, states that for any sequence of random variables, the sum of their standard deviations tends to a limiting value as the sample size increases. This theorem is widely used in statistical inference and is fundamental to many areas of mathematics, including calculus, probability theory, and statistics.

Proof of the central limit theorem:

Let X1, X2,..., Xn be a sample of n random variables, each with a normal distribution N(mu, sigma^2). The Central Limit Theorem states that as the sample size n increases, the sum of the squared standard deviations of the random variables tends to a limiting value as sigma^2.

Letting S = sum(X1^2, X2^2,..., Xn^2), we have:

S = ∑(X1^2 + X2^2 +... + Xn^2)

This is because the sum of the squared standard deviations is a sum of the squared standard deviations, and the sum of the squared standard deviations is a continuous function of the sample size n.

Now, let's consider the limit of the sum of the squared standard deviations as the sample size n increases. This limit is denoted by ∞.

Since the standard deviation of each random variable is a continuous function of the sample size n, we can apply the Central Limit Theorem to each random variable separately. That is, we can find the limiting value of the sum of the squared standard deviations for each random variable.

For example, let X1 = N(mu = 0, sigma^2 = 1), X2 = N(mu = 1, sigma^2 = 1),..., Xn = N(mu = 0, sigma^2 = 1).

Using the Central Limit Theorem, we have:

S = ∑(X1^2 + X2^2 +... + Xn^2)

= ∑(X1^2 + X2^2 +... + Xn^2) + ∑(X1^2 + X2^2 +... + Xn^2)

= ∑(X1^2 + X2^2 +... + Xn^2) + ∑(X1^2 + X2^2 +... + Xn^2)

= ∑(X1^2 + X2^2 +... + Xn^2) + ∑(X1^2 + X2^2 +... + Xn^2)

= ∑(X1^2 + X2^2 +... + Xn^2) + ∑(X1^2 + X2^2 +... + Xn^2)

= ∑(X1^2 + X2^2 +... + Xn^2)

Since the sum of the squared standard deviations is a continuous function of the sample size n, we can find the limiting value of the sum of the squared standard deviations for each random variable.

Therefore, the sum of the squared standard deviations for each random variable tends to a limiting value as the sample size n increases, which is the Central Limit Theorem.
human
Can we make the standard deviation of each random variable is a stand alone function, and how can that be done?
gpt
Yes this can be done. To make the standard deviation of each random variable a stand-alone function, we can define a function that takes in a sequence of random variables and returns the sum of their squared standard deviations. This is an example in Julia : 

function sum_squared_std_devs(X)
    n = length(X)
    s = 0
    for i in 1:n
        s += std(X[i])^2
    end
    return s
end

The above function takes in a sequence of random variables 'X', computes the sample size 'n', initializes a variable 's' to zero, and then iterates over the elements of 'X', computing the standard deviation of each element and adding its squared value to 's'. Finally, the function returns the value of 's'. You can use this function to compute the sum of the squared standard deviations for any sequence for any sequence of random variables in Julia. Would you like to know more?