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.
See also
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 frombayes_mvs
.mvsdist
has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variableSCIPY_ARRAY_API=1
and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.Library
CPU
GPU
NumPy
✅
n/a
CuPy
n/a
⛔
PyTorch
⛔
⛔
JAX
⛔
⛔
Dask
⛔
n/a
See Support for the array API standard for more information.
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