Inverse-chi-squared distribution

Probability distribution
Inverse-chi-squared
Probability density function
Cumulative distribution function
Parameters ν > 0 {\displaystyle \nu >0\!}
Support x ( 0 , ) {\displaystyle x\in (0,\infty )\!}
PDF 2 ν / 2 Γ ( ν / 2 ) x ν / 2 1 e 1 / ( 2 x ) {\displaystyle {\frac {2^{-\nu /2}}{\Gamma (\nu /2)}}\,x^{-\nu /2-1}e^{-1/(2x)}\!}
CDF Γ ( ν 2 , 1 2 x ) / Γ ( ν 2 ) {\displaystyle \Gamma \!\left({\frac {\nu }{2}},{\frac {1}{2x}}\right){\bigg /}\,\Gamma \!\left({\frac {\nu }{2}}\right)\!}
Mean 1 ν 2 {\displaystyle {\frac {1}{\nu -2}}\!} for ν > 2 {\displaystyle \nu >2\!}
Median 1 ν ( 1 2 9 ν ) 3 {\displaystyle \approx {\dfrac {1}{\nu {\bigg (}1-{\dfrac {2}{9\nu }}{\bigg )}^{3}}}}
Mode 1 ν + 2 {\displaystyle {\frac {1}{\nu +2}}\!}
Variance 2 ( ν 2 ) 2 ( ν 4 ) {\displaystyle {\frac {2}{(\nu -2)^{2}(\nu -4)}}\!} for ν > 4 {\displaystyle \nu >4\!}
Skewness 4 ν 6 2 ( ν 4 ) {\displaystyle {\frac {4}{\nu -6}}{\sqrt {2(\nu -4)}}\!} for ν > 6 {\displaystyle \nu >6\!}
Excess kurtosis 12 ( 5 ν 22 ) ( ν 6 ) ( ν 8 ) {\displaystyle {\frac {12(5\nu -22)}{(\nu -6)(\nu -8)}}\!} for ν > 8 {\displaystyle \nu >8\!}
Entropy

ν 2 + ln ( ν 2 Γ ( ν 2 ) ) {\displaystyle {\frac {\nu }{2}}\!+\!\ln \!\left({\frac {\nu }{2}}\Gamma \!\left({\frac {\nu }{2}}\right)\right)}

( 1 + ν 2 ) ψ ( ν 2 ) {\displaystyle \!-\!\left(1\!+\!{\frac {\nu }{2}}\right)\psi \!\left({\frac {\nu }{2}}\right)}
MGF 2 Γ ( ν 2 ) ( t 2 i ) ν 4 K ν 2 ( 2 t ) {\displaystyle {\frac {2}{\Gamma ({\frac {\nu }{2}})}}\left({\frac {-t}{2i}}\right)^{\!\!{\frac {\nu }{4}}}K_{\frac {\nu }{2}}\!\left({\sqrt {-2t}}\right)} ; does not exist as real valued function
CF 2 Γ ( ν 2 ) ( i t 2 ) ν 4 K ν 2 ( 2 i t ) {\displaystyle {\frac {2}{\Gamma ({\frac {\nu }{2}})}}\left({\frac {-it}{2}}\right)^{\!\!{\frac {\nu }{4}}}K_{\frac {\nu }{2}}\!\left({\sqrt {-2it}}\right)}

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. It arises in Bayesian inference, where it can be used as the prior and posterior distribution for an unknown variance of the normal distribution.

Definition

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 X {\displaystyle X} has the chi-squared distribution with ν {\displaystyle \nu } degrees of freedom, then according to the first definition, 1 / X {\displaystyle 1/X} has the inverse-chi-squared distribution with ν {\displaystyle \nu } degrees of freedom; while according to the second definition, ν / X {\displaystyle \nu /X} has the inverse-chi-squared distribution with ν {\displaystyle \nu } degrees of freedom. Information associated with the first definition is depicted on the right side of the page.

The first definition yields a probability density function given by

f 1 ( x ; ν ) = 2 ν / 2 Γ ( ν / 2 ) x ν / 2 1 e 1 / ( 2 x ) , {\displaystyle f_{1}(x;\nu )={\frac {2^{-\nu /2}}{\Gamma (\nu /2)}}\,x^{-\nu /2-1}e^{-1/(2x)},}

while the second definition yields the density function

f 2 ( x ; ν ) = ( ν / 2 ) ν / 2 Γ ( ν / 2 ) x ν / 2 1 e ν / ( 2 x ) . {\displaystyle f_{2}(x;\nu )={\frac {(\nu /2)^{\nu /2}}{\Gamma (\nu /2)}}x^{-\nu /2-1}e^{-\nu /(2x)}.}

In both cases, x > 0 {\displaystyle x>0} and ν {\displaystyle \nu } is the degrees of freedom parameter. Further, Γ {\displaystyle \Gamma } 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 σ 2 = 1 / ν , {\displaystyle \sigma ^{2}=1/\nu ,} while for the second definition σ 2 = 1 {\displaystyle \sigma ^{2}=1} .

Related distributions

  • chi-squared: If X χ 2 ( ν ) {\displaystyle X\thicksim \chi ^{2}(\nu )} and Y = 1 X {\displaystyle Y={\frac {1}{X}}} , then Y Inv- χ 2 ( ν ) {\displaystyle Y\thicksim {\text{Inv-}}\chi ^{2}(\nu )}
  • scaled-inverse chi-squared: If X Scale-inv- χ 2 ( ν , 1 / ν ) {\displaystyle X\thicksim {\text{Scale-inv-}}\chi ^{2}(\nu ,1/\nu )\,} , then X inv- χ 2 ( ν ) {\displaystyle X\thicksim {\text{inv-}}\chi ^{2}(\nu )}
  • Inverse gamma with α = ν 2 {\displaystyle \alpha ={\frac {\nu }{2}}} and β = 1 2 {\displaystyle \beta ={\frac {1}{2}}}
  • Inverse chi-squared distribution is a special case of type 5 Pearson distribution

See also

References

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

External links

  • InvChisquare in geoR package for the R Language.
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