(related to Proposition: Geometric Distribution)

In a Bernoulli experiment, let \(X\) be the random variable having the realization \(k\), if and only if we observe the event \(A\) for the first time at the \(k\)-th repetition of the experiment. The joint event \(X=k\) is given by

\[B_k:=(\underbrace{\overline{A},\ldots,\overline{A}}_{k-1\text{ times}},A).\]

Thus, \(B_k\) is the event of observing \(k-1\) times the complement event \(\overline{A}\) and observing the event \(A\) at the \(k\)-th repetition. By hypothesis, the probability of observing \(A\) in each repetition is \(p:=p(A)\) and all repetitions are mutually independent. Therefore, the probability mass function is given by

\[p(B_k)=p(X = k)=\begin{cases} p(1-p)^{k-1}&\text{for }k=1,2,3,\ldots \\\\ 0&\text{else.}\end{cases}\quad\quad ( * )\]

The geometric distribution (i.e. the probability distribution of the random variable \(X\)) is given by

\[\begin{array}{rcll} p(X \le x)&=&0&\text{for }x < 1\\ p(X \le x)&=&\sum_{k=1}^{k=x}p(1-p)^{k-1}&\text{for }1\le x \end{array}\]

We have the following cases:

Case \(p=0\)

Then we have \(p(B_k)=0\) for all \(k\ge 1\).

Case \(p=1\)

It follows from \( ( * )\) that \(p(B_1)=1\), and \(p(B_k)=0\) for all \(k\ge 2\).

Case \(0 < p < 1\)

Note that the probabilities \(p(B_k)\) are all positive, i.e. all mutually exclusive events \(B_1,B_2,B_3,\ldots\) can occur. On the other hand, it can happen that we will observe neither \(B_1\), nor \(B_2\), nor \(B_3\), etc., for (in an extreme case) infinitely many times.

In this extreme case, the event

\[\bigcup_{k=1}^\infty B_k\]

has the probability following (among others) from geometric progression and the convergence behavior of the sequence \((x^n)_{n\in\mathbb N}\) for \(|x| < 1\):

\[\begin{array}{rcll} p(\bigcup_{k=1}^\infty B_k)&=&\sum_{k=1}^\infty p(B_k)&\text{mutually exclusive events}\\ &=&\lim_{n\to\infty}\sum_{k=1}^n p(B_k)&\text{definition of infinite series}\\ &=&\lim_{n\to\infty}\sum_{k=1}^n p(1-p)^{k-1}&\text{by }( * )\\ &=&\lim_{n\to\infty}p\sum_{k=1}^n (1-p)^{k-1}&\text{distributivity law for real numbers}\\ &=&\lim_{n\to\infty}p\sum_{k=0}^{n-1} (1-p)^{k}&\text{change of summing index}\\ &=&\lim_{n\to\infty}p\frac{1-(1-p)^n}{1-(1-p)}&\text{geometric progression}\\ &=&\lim_{n\to\infty}p\frac{1-(1-p)^n}{p}&\text{distributivity law for real numbers}\\ &=&\lim_{n\to\infty}1-(1-p)^n&\text{cancelling }p\\ &=&1-0&\text{convergence of }(x^n)\text{ for }|x| < 1\\ &=&1 \end{array}\]

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  1. Bosch, Karl: "Elementare Einf├╝hrung in die Wahrscheinlichkeitsrechnung", vieweg Studium, 1995, 6th Edition