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.
This article relies largely or entirely on a single source. (April 2024) |
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shape parameter | ||
| Support | positive-definite real matrix | ||
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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 .