# scipy.stats.mvsdist¶

scipy.stats.mvsdist(data)[source]

‘Frozen’ distributions for mean, variance, and standard deviation of data.

Parameters
dataarray_like

Input array. Converted to 1-D using ravel. Requires 2 or more data-points.

Returns
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.

Notes

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>.mean() and <dist>.interval(0.90) on the three distribution objects returned from this function will give the same results that are returned from bayes_mvs.

References

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


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