Definition:Expectation/Continuous

Definition
Let $X$ be a continuous random variable over the probability space $\struct {\Omega, \Sigma, \Pr}$.

Let $F = \Pr \paren {X < x}$ be the cumulative probability function of $X$.

The expectation of $X$ is written $\operatorname E \paren X$, and is defined over the probability measure as:


 * $\displaystyle \operatorname E \paren X := \int_{x \mathop \in \Omega} x \rd F$

whenever the integral is absolutely convergent, i.e. when:


 * $\displaystyle \int_{x \mathop \in \Omega} \size x \rd F < \infty$

Also, from the definition of probability density function $f_X$ of $X$, we see it can also be written over the sample space:
 * $\displaystyle \operatorname E \paren X := \int_{x \mathop \in \Omega_X} x \ f_X \paren x \rd x$

Also known as
The expectation of $X$ is also called the expected value of $X$ or the mean of $X$, and (for a given continuous random variable) is often denoted $\mu$.

The terminology is appropriate, as it can be seen that an expectation is an example of a normalized weighted mean.

This follows from the fact that a probability density function is a normalized weight function.

Also see
It can also be seen that the expectation of a continuous random variable is its first moment.