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Inverse-chi-squared distribution

In probability and statistics, the inverse-chi-squared distribution (or inverted-chi-square distribution[1]) is a continuous probability distribution of a positive-valued random variable. It is closely related to the chi-squared distribution and its specific importance is that it arises in the application of Bayesian inference to the normal distribution, where it can be used as the prior and posterior distribution for an unknown variance.

Inverse-chi-squared
Probability density function
Inverse chi squared density.png
Cumulative distribution function
Inverse chi squared distribution.png
Parameters
Support
PDF
CDF
Mean for
Median
Mode
Variance for
Skewness for
Ex. kurtosis for
Entropy

MGF ; does not exist as real valued function
CF

DefinitionEdit

The inverse-chi-squared distribution (or inverted-chi-square distribution[1] ) is the probability distribution of a random variable whose multiplicative inverse (reciprocal) has a chi-squared distribution. It is also often defined as the distribution of a random variable whose reciprocal divided by its degrees of freedom is a chi-squared distribution. That is, if   has the chi-squared distribution with   degrees of freedom, then according to the first definition,   has the inverse-chi-squared distribution with   degrees of freedom; while according to the second definition,   has the inverse-chi-squared distribution with   degrees of freedom. Only the first definition will usually be covered in this article.

The first definition yields a probability density function given by

 

while the second definition yields the density function

 

In both cases,   and   is the degrees of freedom parameter. Further,   is the gamma function. Both definitions are special cases of the scaled-inverse-chi-squared distribution. For the first definition the variance of the distribution is   while for the second definition  .

Related distributionsEdit

  • chi-squared: If   and  , then  
  • scaled-inverse chi-squared: If  , then  
  • Inverse gamma with   and  

See alsoEdit

ReferencesEdit

  1. ^ a b Bernardo, J.M.; Smith, A.F.M. (1993) Bayesian Theory ,Wiley (pages 119, 431) ISBN 0-471-49464-X

External linksEdit