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Inverse matrix gamma distribution

In today's article we are going to talk about Inverse matrix gamma distribution. Inverse matrix gamma distribution is a topic that has captured the attention of many in recent years, and it is important to understand its implications and repercussions. From its impact on society to its influence on popular culture, Inverse matrix gamma distribution has proven to be a topic of interest and relevance to a wide range of people. Throughout this article, we will explore different aspects of Inverse matrix gamma distribution and discuss its importance in today's world. We hope this article gives you a more complete understanding of Inverse matrix gamma distribution and its effects in our reality.

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Inverse matrix gamma
Notation
Parameters

shape parameter
scale parameter

scale (positive-definite real matrix)
Support positive-definite real matrix
PDF

In statistics, the inverse matrix gamma distribution is a generalization of the inverse gamma distribution to positive-definite matrices.[1] It is a more general version of the inverse Wishart distribution, and is used similarly, e.g. as the conjugate prior of the covariance matrix of a multivariate normal distribution or matrix normal distribution. The compound distribution resulting from compounding a matrix normal with an inverse matrix gamma prior over the covariance matrix is a generalized matrix t-distribution.[citation needed]

This reduces to the inverse Wishart distribution with degrees of freedom when .

See also

References

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