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<record version="4" id="528">
 <title>exponential random variable</title>
 <name>ExponentialRandomVariable</name>
 <created>2001-10-26 04:17:15</created>
 <modified>2004-04-09 12:43:44</modified>
 <type>Definition</type>
 <creator id="2727" name="mathcam"/>
 <author id="2727" name="mathcam"/>
 <author id="103" name="aparna"/>
 <author id="23" name="Riemann"/>
 <classification>
	<category scheme="msc" code="62E15"/>
 </classification>
 <synonyms>
	<synonym concept="exponential random variable" alias="exponential distribution"/>
 </synonyms>
 <preamble>\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{xypic}</preamble>
 <content>$X$ is a \emph{exponential random variable} with parameter $\lambda&gt;0$ if its probability density function is given for $x&gt;0$ by

\begin{align*}
f_X(x) = \lambda e^{-\lambda x}.
\end{align*}

To denote this, one usually writes $X\sim Exp(\lambda)$.


For an exponential random variable $X$:
\begin{enumerate}
\item $X$ is commonly used to model lifetimes and duration between Poisson events.
\item The expected value of $X$ is given by $E[X] = \frac{1}{\lambda}$
\item The variance of $X$ is given by $Var[X] = \frac{1}{\lambda^2}$
\item The moments of $X$ are given by $M_X(t) = \frac{\lambda}{\lambda - t}$
\item It is interesting to note that $X$ is a gamma random variable with an $\alpha$ parameter of 1.

\end{enumerate}</content>
</record>
