Hi, I was trying to use the random positive definite matrix generator implemented in sklearn ( https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_spd_matrix.html). However, I noticed the documentation is very minimal, without any references on related research articles. I'm wondering if it generates a uniformly random covariance matrix to be used as a sampling method to generate a null distribution for covariances. I tried looking at the source code. But I didn't find any reasons or explanations explaining the method.
In other words, I wanted to know if the random symmetric positive definite matrix returned is uniformly sampled from the space of all positive definite matrices or not. This is the idea I was interested in when I came across make_spd_matrix: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.594.3009&rep=rep1&type=pdf Kind regards, Sina
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