pyuncertainnumber.pba.cbox
==========================

.. py:module:: pyuncertainnumber.pba.cbox


Attributes
----------

.. autoapisummary::

   pyuncertainnumber.pba.cbox.named_cbox
   pyuncertainnumber.pba.cbox.named_nextvalue


Functions
---------

.. autoapisummary::

   pyuncertainnumber.pba.cbox.interval_measurements
   pyuncertainnumber.pba.cbox.infer_cbox
   pyuncertainnumber.pba.cbox.infer_predictive_distribution
   pyuncertainnumber.pba.cbox.CBbernoulli_p
   pyuncertainnumber.pba.cbox.CBbernoulli
   pyuncertainnumber.pba.cbox.CBbinomial_p
   pyuncertainnumber.pba.cbox.CBbinomial
   pyuncertainnumber.pba.cbox.nextvalue_binomialnp
   pyuncertainnumber.pba.cbox.parameter_binomialnp_n
   pyuncertainnumber.pba.cbox.parameter_binomialnp_p
   pyuncertainnumber.pba.cbox.CBpoisson_lambda
   pyuncertainnumber.pba.cbox.CBpoisson
   pyuncertainnumber.pba.cbox.CBexponential_lambda
   pyuncertainnumber.pba.cbox.CBexponential
   pyuncertainnumber.pba.cbox.cboxNormalMu_base
   pyuncertainnumber.pba.cbox.CBnormal_mu
   pyuncertainnumber.pba.cbox.CBnormal_sigma
   pyuncertainnumber.pba.cbox.CBnormal
   pyuncertainnumber.pba.cbox.CBlognormal
   pyuncertainnumber.pba.cbox.CBlognormal_mu
   pyuncertainnumber.pba.cbox.CBlognormal_sigma
   pyuncertainnumber.pba.cbox.CBuniform_midpoint
   pyuncertainnumber.pba.cbox.CBuniform_width
   pyuncertainnumber.pba.cbox.CBuniform_minimum
   pyuncertainnumber.pba.cbox.CBuniform_maximum
   pyuncertainnumber.pba.cbox.CBuniform
   pyuncertainnumber.pba.cbox.CBnonparametric
   pyuncertainnumber.pba.cbox.CBnormal_meandifference
   pyuncertainnumber.pba.cbox.CBnonparametric_deconvolution


Module Contents
---------------

.. py:function:: interval_measurements(func)

   decorator for incorporating interval valued data


.. py:function:: infer_cbox(family: str, data, **args) -> pyuncertainnumber.pba.cbox_constructor.Cbox

   top-level call signature to infer a c-box given data and family, plus rarely additional kwargs

   .. rubric:: Notes

   - data (list): a list of data samples, e.g. [2]
   - additina kwargs such as N for binomial family

   .. rubric:: Example

   >>> infer_cbox('binomial', data=[2], N=10)


.. py:function:: infer_predictive_distribution(family: str, data, **args)

   top-level call for the next value predictive distribution


.. py:function:: CBbernoulli_p(x)

.. py:function:: CBbernoulli(x)

.. py:function:: CBbinomial_p(x, N)

   cbox for Bionomial parameter

   :param x: sample data as in a list of success or number of success or
             a single int as the number of success k
   :type x: list or int
   :param N: number of trials
   :type N: int

   .. note::

      x[i] ~ binomial(N, p), for unknown p, x[i] is a nonnegative integer
      but x is a int number, it suggests the number of success as `k`.

   :returns: cbox object
   :rtype: cbox


.. py:function:: CBbinomial(x, N)

.. py:function:: nextvalue_binomialnp(x)

.. py:function:: parameter_binomialnp_n(x)

.. py:function:: parameter_binomialnp_p(x)

.. py:function:: CBpoisson_lambda(x)

.. py:function:: CBpoisson(x)

.. py:function:: CBexponential_lambda(x)

.. py:function:: CBexponential(x)

.. py:function:: cboxNormalMu_base(x)

   base function for precise sample x


.. py:function:: CBnormal_mu(x, style='analytical')

   :param x: (array-like) the sample data
   :param style: (str) the style of the output CDF, either 'analytical' or 'samples'
   :param size: (int) the discritisation size.
                meaning the no of ppf in analytical style and the no of MC samples in samples style

   :returns: (array-like) the CDF of the normal distribution
   :rtype: CDF


.. py:function:: CBnormal_sigma(x)

.. py:function:: CBnormal(x)

.. py:function:: CBlognormal(x)

.. py:function:: CBlognormal_mu(x)

.. py:function:: CBlognormal_sigma(x)

.. py:function:: CBuniform_midpoint(x)

.. py:function:: CBuniform_width(x)

.. py:function:: CBuniform_minimum(x)

.. py:function:: CBuniform_maximum(x)

.. py:function:: CBuniform(x)

.. py:function:: CBnonparametric(x)

.. py:function:: CBnormal_meandifference(x1, x2)

.. py:function:: CBnonparametric_deconvolution(x, error)

.. py:data:: named_cbox

.. py:data:: named_nextvalue