Template:Exponential Distribution Definition: Difference between revisions
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with two parameters, namely <span class="texhtml">λ</span> and <span class="texhtml">γ</span> (this form is also known as the ''2-parameter exponential''). If the location parameter, <span class="texhtml">γ</span>, is assumed to be zero, then the distribution becomes the 1-parameter exponential or: <br> | with two parameters, namely <span class="texhtml">λ</span> and <span class="texhtml">γ</span> (this form is also known as the ''2-parameter exponential''). <math \lambda</math> is the constant failure rate in failures per unit of measurement (e.g., failures per hour, per cycle, etc.). In addition, <math>\lambda =\tfrac{1}{m}</math>, where <math>{m}</math> is the mean time between failures (or to failure). | ||
If the location parameter, <span class="texhtml">γ</span>, is assumed to be zero, then the distribution becomes the 1-parameter exponential or: <br> | |||
Revision as of 03:23, 7 August 2012
The exponential distribution is commonly used for components or systems exhibiting a constant failure rate. Due to its simplicity, it has been widely employed, even in cases where it doesn't apply. It is defined in its most general case by:
- [math]\displaystyle{ \begin{align} f(t)=\lambda e^{-\lambda (t-\gamma)} \end{align} }[/math]
with two parameters, namely λ and γ (this form is also known as the 2-parameter exponential). [math]\displaystyle{ is the constant failure rate in failures per unit of measurement (e.g., failures per hour, per cycle, etc.). In addition, \lt math\gt \lambda =\tfrac{1}{m} }[/math], where [math]\displaystyle{ {m} }[/math] is the mean time between failures (or to failure).
If the location parameter, γ, is assumed to be zero, then the distribution becomes the 1-parameter exponential or:
- [math]\displaystyle{ \begin{align} f(t)=\lambda e^{-\lambda t} \end{align} }[/math]
For a detailed discussion of this distribution, see The Exponential Distribution.