# multivariate_t

sherpa.sim.sample.multivariate_t(mean, cov, df[, size])[source] [edit on github]

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.

Parameters
• 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,k)`, `m*n*k` samples are generated, and packed in an `m`-by-`n`-by-`k` arrangement. 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.

Returns

• out (ndarray) – The drawn samples, of shape size, if that was provided. If not, the shape is `(N,)`.

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

• the better