Vous êtes un professionnel et vous avez besoin d'une formation ? Deep Learning avec Python
et Keras et Tensorflow Voir le programme détaillé

Module « scipy.stats »

Fonction mvsdist - module scipy.stats

Signature de la fonction mvsdist

def mvsdist(data) 

Description

help(scipy.stats.mvsdist)

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

Parameters
----------
data : array_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

Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé