‘Frozen’ distributions for mean, variance, and standard deviation of data.
data : array_like
Input array. Converted to 1-D using ravel. Requires 2 or more data-points.
mdist : “frozen” distribution object
Distribution object representing the mean of the data
vdist : “frozen” distribution object
Distribution object representing the variance of the data
sdist : “frozen” distribution object
Distribution object representing the standard deviation of the data
The return values from
bayes_mvs(data)is equivalent to
tuple((x.mean(), x.interval(0.90)) for x in mvsdist(data)).
In other words, calling
<dist>.interval(0.90)on the three distribution objects returned from this function will give the same results that are returned from
T.E. Oliphant, “A Bayesian perspective on estimating mean, variance, and standard-deviation from data”, http://scholarsarchive.byu.edu/facpub/278, 2006.
>>> 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