Gamma |
public class GammaDistribution : ContinuousDistribution
The GammaDistribution type exposes the following members.
| Name | Description | |
|---|---|---|
| GammaDistribution | Initializes a new instance of the GammaDistribution class | |
| GammaDistribution(Generator) | Initializes a new instance of the GammaDistribution class | |
| GammaDistribution(Double, Double) | Initializes a new instance of the GammaDistribution class | |
| GammaDistribution(Double, Double, Generator) | Initializes a new instance of the GammaDistribution class |
| Name | Description | |
|---|---|---|
| Alpha | Gets or sets the parameter alpha which is used for generation of gamma distributed random numbers. | |
| CanReset |
Gets a value indicating whether the random number distribution can be reset, so that it produces the same
random number sequence again.
(Inherited from Distribution) | |
| Generator |
Gets or sets a Generator object that can be used as underlying random number generator.
(Inherited from Distribution) | |
| Location | ||
| Maximum |
Gets the maximum possible value of gamma distributed random numbers.
(Overrides DistributionMaximum) | |
| Mean |
Gets the mean value of gamma distributed random numbers.
(Overrides DistributionMean) | |
| Median |
Gets the median of gamma distributed random numbers.
(Overrides DistributionMedian) | |
| Minimum |
Gets the minimum possible value of gamma distributed random numbers.
(Overrides DistributionMinimum) | |
| Mode |
Gets the mode of gamma distributed random numbers.
(Overrides DistributionMode) | |
| Order | ||
| Theta | Gets or sets the parameter theta which is used for generation of gamma distributed random numbers. | |
| Variance |
Gets the variance of gamma distributed random numbers.
(Overrides DistributionVariance) |
| Name | Description | |
|---|---|---|
| CDF(Double) | (Overrides ContinuousDistributionCDF(Double)) | |
| CDF(Double, Double, Double) | ||
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) | |
| Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) | |
| GetHashCode | Serves as the default hash function. (Inherited from Object) | |
| GetType | Gets the Type of the current instance. (Inherited from Object) | |
| Initialize | ||
| IsValidAlpha | Determines whether the specified value is valid for parameter Alpha. | |
| IsValidTheta | Determines whether the specified value is valid for parameter Theta. | |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) | |
| NextDouble | (Overrides DistributionNextDouble) | |
| PDF(Double) | (Overrides ContinuousDistributionPDF(Double)) | |
| PDF(Double, Double, Double) | ||
| Quantile(Double) | (Overrides ContinuousDistributionQuantile(Double)) | |
| Quantile(Double, Double, Double) | ||
| Reset |
Resets the random number distribution, so that it produces the same random number sequence again.
(Inherited from Distribution) | |
| ToString | Returns a string that represents the current object. (Inherited from Object) |
| Name | Description | |
|---|---|---|
| _invTheta | ||
| algorithmGD | ||
| alpha | ||
| b | ||
| c | ||
| d | ||
| exponentialDistribution | ||
| generator |
Stores a Generator object that can be used as underlying random number generator.
(Inherited from Distribution) | |
| normalDistribution | ||
| q0 | ||
| r | ||
| s | ||
| s2 | ||
| scale | ||
| si | ||
| theta |
Return Gamma distributed random deviates according to:
a-1 -bx
b (bx) e
p (x) dx = ---------------- dx for x > 0
a,b Gamma(a)
= 0 otherwise
//
The arguments must satisfy the conditions:
a > 0 (positive)
b != 0 (non-zero)
References:
For parameter a >= 1 corresponds to algorithm GD in:
J. H. Ahrens and U. Dieter, Generating Gamma Variates by a
Modified Rejection Technique, Comm. ACM, 25, 1, 47-54 (1982).
For parameter 0 < a < 1 corresponds to algorithm GS in:
J. H. Ahrens and U. Dieter, Computer Methods for Sampling
from Gamma, Beta, Poisson and Binomial Distributions,
Computing, 12, 223-246 (1974).