public class WeibullDistribution extends ContinuousDistribution
Meaning: | The Weibull distribution is often used to model internet traffic because of its heavy tail. |
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Parameters: |
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PDF: | \(P(T=t) = f(t) = \alpha \cdot \beta^{-\alpha} \cdot t^{\alpha -1} \cdot exp(-(\frac{t}{\beta})^{\alpha}) \mbox{ for } t>0 \) |
DF: | \(P(T \le t) = F(t) = 1- exp(-(\frac{t}{\beta})^{\alpha}) \mbox{ for } t>0\) |
Expected value: | \(E[T]= \frac{\beta}{\alpha} \cdot \Gamma(\frac{1}{\alpha}\), whereas \(\Gamma(x)\) is the gamma function |
Variance: | \(VAR[T]= \frac{\beta^2}{\alpha} \cdot \left\{ 2\Gamma(\frac{2}{\alpha}) - \frac{1}{\alpha} \cdot \Gamma(\frac{1}{\alpha})^2 \right\} \) |
Coefficient of variation: | \(c_T= \sqrt{\frac{2\alpha\Gamma(\frac{2}{\alpha})}{(\Gamma(\frac{1}{\alpha}))^2} -1}\) |
Parser example: |
[...].Distribution = Weibull
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Modifier and Type | Field and Description |
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double |
alphaReciprocal |
double |
beta |
rng
CREATE_INSTANCE_METHOD_NAME
Constructor and Description |
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WeibullDistribution(double alpha,
double beta) |
WeibullDistribution(double alpha,
double beta,
RandomNumberGenerator rng) |
Modifier and Type | Method and Description |
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static WeibullDistribution |
createInstance(SimNode ownNode,
Parameters pars,
RandomNumberGenerator rng)
as required by
ReflectionConstructable |
double |
next()
Create random numbers
|
getDefaultRNG, getRandomNumberGenerator, reset
public WeibullDistribution(double alpha, double beta, RandomNumberGenerator rng)
public WeibullDistribution(double alpha, double beta)
public static WeibullDistribution createInstance(SimNode ownNode, Parameters pars, RandomNumberGenerator rng)
ReflectionConstructable
public double next()
ContinuousDistribution
next
in class ContinuousDistribution