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Matrix gamma distribution

From Wikipedia, the free encyclopedia
Generalization of gamma distribution
Matrix gamma
NotationMGp(α,β,Σ){\displaystyle {\rm {MG}}_{p}(\alpha ,\beta ,{\boldsymbol {\Sigma }})}
Parameters

α>p12{\displaystyle \alpha >{\frac {p-1}{2}}}shape parameter (real)
β>0{\displaystyle \beta >0}scale parameter

Σ{\displaystyle {\boldsymbol {\Sigma }}}scale (positive-definite realp×p{\displaystyle p\times p}matrix)
SupportX{\displaystyle \mathbf {X} }positive-definite realp×p{\displaystyle p\times p}matrix
PDF

|Σ|αβpαΓp(α)|X|αp+12exp(tr(1βΣ1X)){\displaystyle {\frac {|{\boldsymbol {\Sigma }}|^{-\alpha }}{\beta ^{p\alpha }\,\Gamma _{p}(\alpha )}}|\mathbf {X} |^{\alpha -{\frac {p+1}{2}}}\exp \left({\rm {tr}}\left(-{\frac {1}{\beta }}{\boldsymbol {\Sigma }}^{-1}\mathbf {X} \right)\right)}

Instatistics, amatrix gamma distribution is a generalization of thegamma distribution topositive-definite matrices.[1] It is effectively a different parametrization of theWishart distribution, and is used similarly, e.g. as theconjugate prior of theprecision matrix of amultivariate normal distribution andmatrix normal distribution. Thecompound distribution resulting from compounding a matrix normal with a matrix gamma prior over the precision matrix is ageneralized matrix t-distribution.[1]

A matrix gamma distributions is identical to aWishart distribution withβΣ=2V,α=n2.{\displaystyle \beta {\boldsymbol {\Sigma }}=2V,\alpha ={\frac {n}{2}}.}

Notice that the parametersβ{\displaystyle \beta } andΣ{\displaystyle {\boldsymbol {\Sigma }}} are not identified; the density depends on these two parameters through the productβΣ{\displaystyle \beta {\boldsymbol {\Sigma }}}.

See also

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Notes

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  1. ^abIranmanesh, Anis, M. Arashib and S. M. M. Tabatabaey (2010)."On Conditional Applications of Matrix Variate Normal Distribution".Iranian Journal of Mathematical Sciences and Informatics, 5:2, pp. 33–43.

References

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  • Gupta, A. K.; Nagar, D. K. (1999)Matrix Variate Distributions, Chapman and Hall/CRCISBN 978-1584880462
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