mvsdist — SciPy v1.15.2 Manual (original) (raw)

scipy.stats.

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 totuple((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