The De Moivre-Laplace Theorem
De Moivre–Laplace theorem, which is a special case of the central limit theorem, states that the normal distribution may be used as an approximation to the binomial distribution under certain conditions. The theorem appeared in the second edition of The Doctrine of Chances by Abraham de Moivre, published in 1738. Although de Moivre did not use the term "Bernoulli trials", he wrote about the probability distribution of the number of times "heads" appears when a coin is tossed 3600 times.
THE DE MOIVRE-LAPLACE THEOREM
If then as
Let be i.i.d. random variables such that
and let . In words, we are betting 1 dollar on the flipping of a fair coin and is our winnings at time .
Let be as above, we are interested in the distribution of the partial sums
To make the combinatorics transparent, it is convenient to consider the probability . The event happens if and only if among the first variables there are exactly values equal to and values equal to . Therefore
The binomial coefficient counts the number of such outcomes, and the factor is the probability of each one.
If we visualize these probabilities and then increase , a clear pattern starts to emerge: the binomial probabilities form a bell-shaped curve, and this curve becomes smoother as grows. The transition from a discrete, jagged distribution to a smooth symmetric bell curve is the phenomenon that the De Moivre-Laplace theorem makes precise.
The foolowing proof is taken from [Durrett2019] p. 98-99
The proof strategy for the theorem is as follows: initially, we approximate the probability for a single point. Then, we proceed to calculate the probability for the interval .
LEMMA
If then .
By Stirling's formula we have
where means as . Now we have
and we get
The first two terms on the right can be transformed by the formula for the difference of two squares
We repeatedly use the fact that if and , then
Assume that
Equivalently, , so
For this choice of , so
and putting things together gives:
Our next step is to compute
Changing variables , we have , and the above is
where
and . Thus, if is large the sum above is
The integrand is the density of the (standard) normal distribution, so changing notation we can write the last quantity as where is a random variable with that distribution. We proved the De Moivre-Laplace Theorem. To remove the restriction to even integers observe