- sherpa.sim.sample.multivariate_t(mean, cov, df[, size])
Draw random deviates from a multivariate Student’s T distribution Such a distribution is specified by its mean covariance matrix, and degrees of freedom. These parameters are analogous to the mean (average or “center”), variance (standard deviation, or “width,” squared), and the degrees of freedom of the one-dimensional t distribution.
mean (1-D array_like, length N) – Mean of the N-dimensional distribution
cov (2-D array_like, shape (N, N)) – Covariate matrix of the distribution. Must be symmetric and positive semi-definite for “physically meaningful” results.
df (int) – Degrees of freedom of the distribution
size (tuple of ints, optional) – Given a shape of, for example,
m*n*ksamples are generated, and packed in an
karrangement. Because each sample is
N-dimensional, the output shape is
(m,n,k,N). If no shape is specified, a single (
N-D) sample is returned.
out (ndarray) – The drawn samples, of shape size, if that was provided. If not, the shape is
In other words, each entry
out[i,j,...,:]is an N-dimensional value drawn from the distribution.
Is this right? This needs to be checked! A reference to the literature