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scipy.stats.

# mvsdist

scipy.stats.mvsdist(data)

  ‘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.

  See also

  `bayes_mvs`

  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