# Expectation of Bernoulli Distribution

## Theorem

Let $X$ be a discrete random variable with a Bernoulli distribution with parameter $p$.

Then the expectation of $X$ is given by:

$\expect X = p$

## Proof 1

From the definition of expectation:

$\ds \expect X = \sum_{x \mathop \in \Img X} x \map \Pr {X = x}$

By definition of Bernoulli distribution:

$\expect X = 1 \times p + 0 \times \paren {1 - p}$

Hence the result.

$\blacksquare$

## Proof 2

Follows directly from Expectation of Binomial Distribution, putting $n = 1$.

$\blacksquare$

## Proof 3

From the Probability Generating Function of Bernoulli Distribution, we have:

$\map {\Pi_X} s = q + p s$

where $q = 1 - p$.

From Expectation of Discrete Random Variable from PGF, we have:

$\expect X = \map { {\Pi_X}'} 1$
$\map { {\Pi_X}'} s = p$

Hence the result.

$\blacksquare$

## Proof 4

From Moment Generating Function of Bernoulli Distribution, the moment generating function of $X$, $M_X$, is given by:

$\map {M_X} t = q + p e^t$

where $q = 1 - p$.

$\expect X = \map {M_X'} 0$

We have:

 $\ds \map {M_X'} t$ $=$ $\ds \frac \d {\d t} \paren {q + p e^t}$ $\ds$ $=$ $\ds p e^t$ Derivative of Constant, Derivative of Exponential Function

Setting $t = 0$ gives:

 $\ds \expect X$ $=$ $\ds p e^0$ $\ds$ $=$ $\ds p$ Exponential of Zero

$\blacksquare$