Template:Random Number Generator Probability Density Functions
The random number generator used for all probability functions is from the C++ standard library (http://www.cplusplus.com/reference/random/).
The Probability Density functions used for the Noise generation are shown in the following table. In all cases the following convention is used:
- [math]\displaystyle{ \mu }[/math] = Mean Value
- [math]\displaystyle{ \sigma }[/math] = Standard Deviation
For all methods, the tag PlantModel.RandomSeedAtStart (set on the Plant Model - FlwSolve page) affects the repeatability of the random values generated. For the same seed value, a repeatable set of random numbers will be generated. For a different seed, a different set of random numbers will be generated. If "*" is used, then a new random set of values will be generated on every run.
Type | Probability Density Function P(t) | NOTES | |
---|---|---|---|
Gaussian |
|
where [math]\displaystyle{ \sigma \gt 0 }[/math] | |
Flat |
|
where [math]\displaystyle{ t \lt \mu - m }[/math] or [math]\displaystyle{ t \gt \mu + m }[/math] | |
|
where [math]\displaystyle{ \mu - m ≤ t ≤; \mu + m }[/math] and [math]\displaystyle{ m ≥; 0 }[/math] and [math]\displaystyle{ m }[/math] = maximum deviation |
||
Poisson |
|
where [math]\displaystyle{ e }[/math] = the base of the natural logarithm system (2.71828...) and [math]\displaystyle{ \mu \gt 0 }[/math] | |
Gamma |
|
where [math]\displaystyle{ \alpha ≥ 0.5 }[/math] and [math]\displaystyle{ \beta \gt 0 }[/math] |
Note: The Chi-squared distribution is a special case of the Gamma distribution |
Weibull |
|
where [math]\displaystyle{ t = \alpha (-\ln(U))^{1/\beta} }[/math] and [math]\displaystyle{ \alpha ≥ 0.04 }[/math] and [math]\displaystyle{ \beta ≥ 0.01 }[/math] |
|
Bernoulli |
|
where [math]\displaystyle{ 0 ≤ p ≤ 1 }[/math] |
|
Binomial |
|
where [math]\displaystyle{ n }[/math] is a positive integer, [math]\displaystyle{ \dbinom{n}{t} = \cfrac{n!}{t!(n-t)!} }[/math] and [math]\displaystyle{ 0 \lt p ≤ 1 }[/math] |
|
Geometric |
|
where [math]\displaystyle{ 0 \lt p ≤ 1 }[/math] | |
Exponential |
|
where [math]\displaystyle{ \mu \gt 0 }[/math] |
|
Extreme Value |
|
where [math]\displaystyle{ z(t) = e^{(\alpha-t)/\beta} }[/math] and [math]\displaystyle{ \beta \gt 0 }[/math] | |
Log Normal |
|
where [math]\displaystyle{ 0 \lt s ≤ 50.70 }[/math] | |
Cauchy |
|
where [math]\displaystyle{ \beta \gt 0 }[/math] | |
Fisher F |
|
where [math]\displaystyle{ 1 ≤ m ≤ 100 }[/math] and [math]\displaystyle{ n ≥ 1 }[/math] | |
Student t |
|
where [math]\displaystyle{ n ≥ 0.25 }[/math] |
References
- Press W.H, Teukolsky S.A, Vetterling W.T, Flannery B.P Numerical Recipes in C (2nd Edition) Cambridge University Press 1992.
- Sanders D.H Statistics A Fresh Approach McGraw-Hill 1990.
- http://www.cplusplus.com/reference/random/ and related links describing each distribution.