Bernoulli Process as Geometric Distribution/Shifted

Theorem
Let $\left \langle{Y_i}\right \rangle$ be a Bernoulli process with parameter $p$.

Let $\mathcal E$ be the experiment which consists of performing the Bernoulli trial $Y_i$ as many times as it takes to achieve a success, and then stop.

Let $k$ be the number of Bernoulli trials to achieve a success.

Then $k$ is modelled by a shifted geometric distribution with parameter $p$.

Proof
Follows directly from the definition of shifted geometric distribution.

Let $Y$ be the discrete random variable defined as the number of trials for the first success to be achieved.

Thus the last trial (and the last trial only) will be a success, and the others will be failures.

The probability that $k-1$ failures are followed by a success is:
 * $\Pr \left({Y = k}\right) = \left({1 - p}\right)^{k-1} p$

Hence the result.