How to prove that geometric distributions converge to an exponential distribution? How to prove that geometric distributions converge to an exponential distribution?


To solve this, I am trying to define an indexing $n$/$m$ and to send $m$ to infinity, but I get zero, not some relevant distribution. What is the technique or approach one must use here?
Recall pmf of the geometric distribution: $\mathbb{P}(X = k) = p (1-p)^k$ for $k \geq 0$.

The geometric distribution has the interpretation of the number of failures in a sequence of Bernoulli trials until the first success. 

Consider a regime when the probability of success is very small, such that $n p = \lambda$, and consider $x = \frac{k}{n}$. Then, in the large $n$ limit:
$$
  1 = \sum_{k=0}^\infty \mathbb{P}(X = k) = \sum_{k=0}^\infty \lambda \left(\left(1 -  \frac{\lambda}{n} \right)^{n \cdot k/n} \frac{1}{n} \right) \stackrel{n \to \infty}\rightarrow \int_0^\infty \lambda \mathrm{e}^{-\lambda x} \mathrm{d} x
$$

Alternatively, you could look at the moment generating function for the geometric distribution:
$$
    \mathcal{M}(p, t) = \frac{p}{1-\mathrm{e}^t (1-p)}
$$
To recover the mgf of the exponential distribution consider the limit:
$$
   \lim_{n \to \infty} \mathcal{M}\left( \frac{\lambda}{n}, \frac{t}{n} \right) = 
   \lim_{n \to \infty} \frac{\lambda}{n - \mathrm{e}^{t/n}\left(n - \lambda\right)} =
   \lim_{n \to \infty} \frac{\lambda}{n \left(1 - \mathrm{e}^{t/n}\right) + \lambda \mathrm{e}^{t/n} } = \frac{\lambda}{\lambda-t}
$$
which is the mgf of the exponential distribution with rate $\lambda$.
I think we can just expand 1-p in Taylor series for small p we get the exponential function.:)
1-p=exp(-p) for very small p (around p=0)such that np=constant. so we find:

p(x)=(1-p)^x.p is becoming p exp(-px) for a continuous random variable x, this in part has the continuous exponential distribution function:)