public class RayleighDistribution extends ContinuousDistribution
| Meaning: | If the values of a two dimensional probability vector are independently normal distributed, then the absolute value of this vector is rayleigh distributed |
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| Parameters: | \(\sigma\) |
| PDF: | \(P(T=t) = f(t) = \begin{cases} \frac{x}{\sigma^2}\exp{-\frac{x^2}{2\sigma^2}} &\mbox{for } x \ge 0 \\ 0 &\mbox{else } \end{cases}\) |
| DF: | \(P(T \le t) = F(t) = \begin{cases} 1 - \exp{-\frac{x^2}{2\sigma^2}} &\mbox{for } x \ge 0 \\ 0 &\mbox{else } \end{cases}\) |
| Expected value: | \(E[T]= \sigma \sqrt{\frac{\pi}{2}}\) |
| Variance: | \(VAR[T]= \frac{(4-\pi}{2}\sigma^2\) |
| Coefficient of variation: | \(c_T= \sqrt{\frac{4-\pi}{2}}\) |
| Parser example: |
[...].Distribution = ikr.simlib.distributions.continuous.RayleighDistribution
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rngCREATE_INSTANCE_METHOD_NAME| Constructor and Description |
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RayleighDistribution(double sigma,
RandomNumberGenerator rng) |
| Modifier and Type | Method and Description |
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static RayleighDistribution |
createInstance(SimNode ownNode,
Parameters pars,
RandomNumberGenerator rng)
as required by
ReflectionConstructable |
double |
next()
Create random numbers
|
getDefaultRNG, getRandomNumberGenerator, resetpublic RayleighDistribution(double sigma,
RandomNumberGenerator rng)
public static RayleighDistribution createInstance(SimNode ownNode, Parameters pars, RandomNumberGenerator rng)
ReflectionConstructablepublic double next()
ContinuousDistributionnext in class ContinuousDistribution