Inverse Gamma

The inverse gamma distribution is a two-parameter family of continuous probability distributions on the positive real line, which is the distribution of the reciprocal of a variable distributed according to the gamma distribution. It has a scale parameter θ and a shape parameter k.

This topic contains the following sections:

Constructor
Methods
Properties

Constructor

Constructor	Description	Performance
set parameters	Creates new instance of InverseGamma with user specified parameters. InverseGamma(Double, Double)

Description

Performance

set parameters

Creates new instance of InverseGamma with user specified parameters.

method InverseGamma(Double, Double)

Methods

Method	Description	Performance
pdf	Probability density function. where Γ denotes the Gamma function. Pdf(Double) Static probability density function. Pdf(Double, Double, Double)
cdf	Cumulative distribution function. Cdf(Double) Static cumulative distribution function. Cdf(Double, Double, Double)
inverse cdf	Inverse cumulative distribution function. Is useful for quantile calculation. Function will return value of random variable such that likelihood for this random variable to occur less than or equal to returned value equal to specified pvalue parameter. InverseCdf(Double, Double, Double)

Method

Description

Performance

pdf

Probability density function.

where Γ denotes the Gamma function.

method Pdf(Double)

Static probability density function.

Static Pdf(Double, Double, Double)

cdf

Cumulative distribution function.

method Cdf(Double)

Static cumulative distribution function.

Static Cdf(Double, Double, Double)

inverse cdf

Inverse cumulative distribution function. Is useful for quantile calculation. Function will return value of random variable such that likelihood for this random variable to occur less than or equal to returned value equal to specified pvalue parameter.

Static InverseCdf(Double, Double, Double)

Properties

Property	Description	Performance
shape	Shape of the distribution. Shape
scale	Scale of the distribution. Scale
mean	Mean of the distribution. Mean
mode	Mode of distribution. Mode
kurtosis	Excess Kurtosis of distribution. Kurtosis
skewness	Skewness of distribution. Skewness
variance	Variance of the distribution. Variance