Inverse Chi Square Distribution
The inverse chi square (χ2) distribution, also known as the inverted chi square distribution, is a continuous probability distribution that models the distribution of the reciprocal (inverse) of a chi square variable. It is commonly used in Bayesian statistics.
Definition
The inverse chi square distribution with
The probability density function (PDF) is given by:
The cumulative distribution function (CDF) can be expressed in terms of the gamma function:
The distribution is defined for positive real values (
Applications
Bayesian inference serves as a conjugate prior for the variance parameter in normal distributions.
Statistical quality control uses this distribution for variance component analysis.
Reliability engineering employs the distribution in lifetime modeling.
Properties
Property | Value |
---|---|
Mean | |
Mode | |
Variance | |
Skewness | |
Excess Kurtosis | |
Entropy |
Relationships to Other Distributions
If X follows a chi-square distribution with
degrees of freedom, then 1/X follows an inverse chi-square distribution with degrees of freedom.The inverse chi-square distribution is a special case of the inverse gamma distribution, equivalent to an inverse gamma distribution with shape parameter
and scale parameter .The scaled inverse chi-square distribution is obtained by scaling the inverse chi-square distribution by a factor.
The InverseChiSquareDistribution class
The inverse chi square distribution is implemented by the InverseChiSquareDistribution class. It has one constructor which takes the degrees of freedom as its only argument. The following constructs an inverse chi square distribution with 10 degrees of freedom:
var invChiSquare = new ChiSquareDistribution(10);
The InverseChiSquareDistribution class has one specific property, DegreesOfFreedom, that returns the degrees of freedom of the distribution.
References
For more information on the inverse chi-squared distribution, refer to the following sources:
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). "Continuous Univariate Distributions, Volume 1", Chapter 18. Wiley Series in Probability and Statistics.
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). "Continuous Univariate Distributions, Volume 2", Chapter 29. Wiley Series in Probability and Statistics.