---
T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
standard-deviation from data", https://scholarsarchive.byu.edu/facpub/278,
2006.

Examples
--------
>>> from scipy import stats
>>> data = [6, 9, 12, 7, 8, 8, 13]
>>> mean, var, std = stats.mvsdist(data)

We now have frozen distribution objects "mean", "var" and "std" that we can
examine:

>>> mean.mean()
9.0
>>> mean.interval(0.95)
(6.6120585482655692, 11.387941451734431)
>>> mean.std()
1.1952286093343936

