WISDoM: a framework for the Analysis of Wishart distributed matrices

Mengucci, Carlo, Remondini, Daniel, Giampieri, Enrico

arXiv.org Machine Learning 

APPENDIX A. Visualizing the Wishart Distribution The Wishart distribution is a generalization to multiple dimensions of the chi-squared distribution, or in the case of non-integer degrees of freedom, of the gamma distribution. We show in fig.5 that for a 1-dimensional and equal to 1 Σ scale matrix, the Wishart distribution W 1( n, 1) is equivalent to the χ 2 ( n) distribution. Figure 5: Monodimensional Wishart Distribution and χ 2 (n) distribution comparison Save for this simple case, being the Wishart a distribution over matrices, it is a generally hard task to visualize it as a density function. Samples can be however drawn from it and the eigenvectors and eigenvalues of the resulting sampled matrix can be exploited to define an ellipse. An example of this technique is shown in fig.6.

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