pyuncertainnumber.pba.cbox¶

Attributes¶

`named_cbox`
`named_nextvalue`

Functions¶

`interval_measurements`(func)	decorator for incorporating interval valued data
`infer_cbox`(→ pyuncertainnumber.pba.cbox_constructor.Cbox)	top-level call signature to infer a c-box given data and family, plus rarely additional kwargs
`infer_predictive_distribution`(family, data, **args)	top-level call for the next value predictive distribution
`CBbernoulli_p`(x)
`CBbernoulli`(x)
`CBbinomial_p`(x, N)	cbox for Bionomial parameter
`CBbinomial`(x, N)
`nextvalue_binomialnp`(x)
`parameter_binomialnp_n`(x)
`parameter_binomialnp_p`(x)
`CBpoisson_lambda`(x)
`CBpoisson`(x)
`CBexponential_lambda`(x)
`CBexponential`(x)
`cboxNormalMu_base`(x)	base function for precise sample x
`CBnormal_mu`(x[, style])
`CBnormal_sigma`(x)
`CBnormal`(x)
`CBlognormal`(x)
`CBlognormal_mu`(x)
`CBlognormal_sigma`(x)
`CBuniform_midpoint`(x)
`CBuniform_width`(x)
`CBuniform_minimum`(x)
`CBuniform_maximum`(x)
`CBuniform`(x)
`CBnonparametric`(x)
`CBnormal_meandifference`(x1, x2)
`CBnonparametric_deconvolution`(x, error)

Module Contents¶

pyuncertainnumber.pba.cbox.interval_measurements(func)¶: decorator for incorporating interval valued data

pyuncertainnumber.pba.cbox.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

Notes

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

Example

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

pyuncertainnumber.pba.cbox.infer_predictive_distribution(family: str, data, **args)¶: top-level call for the next value predictive distribution

pyuncertainnumber.pba.cbox.CBbernoulli_p(x)¶

pyuncertainnumber.pba.cbox.CBbernoulli(x)¶

pyuncertainnumber.pba.cbox.CBbinomial_p(x, N)¶

cbox for Bionomial parameter

Parameters:

x (list or int) – sample data as in a list of success or number of success or a single int as the number of success k
N (int) – number of trials

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
Return type:: cbox

pyuncertainnumber.pba.cbox.CBbinomial(x, N)¶

pyuncertainnumber.pba.cbox.nextvalue_binomialnp(x)¶

pyuncertainnumber.pba.cbox.parameter_binomialnp_n(x)¶

pyuncertainnumber.pba.cbox.parameter_binomialnp_p(x)¶

pyuncertainnumber.pba.cbox.CBpoisson_lambda(x)¶

pyuncertainnumber.pba.cbox.CBpoisson(x)¶

pyuncertainnumber.pba.cbox.CBexponential_lambda(x)¶

pyuncertainnumber.pba.cbox.CBexponential(x)¶

pyuncertainnumber.pba.cbox.cboxNormalMu_base(x)¶: base function for precise sample x

pyuncertainnumber.pba.cbox.CBnormal_mu(x, style='analytical')¶

Parameters:

x – (array-like) the sample data
style – (str) the style of the output CDF, either ‘analytical’ or ‘samples’
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

Return type:

CDF

pyuncertainnumber.pba.cbox.CBnormal_sigma(x)¶

pyuncertainnumber.pba.cbox.CBnormal(x)¶

pyuncertainnumber.pba.cbox.CBlognormal(x)¶

pyuncertainnumber.pba.cbox.CBlognormal_mu(x)¶

pyuncertainnumber.pba.cbox.CBlognormal_sigma(x)¶

pyuncertainnumber.pba.cbox.CBuniform_midpoint(x)¶

pyuncertainnumber.pba.cbox.CBuniform_width(x)¶

pyuncertainnumber.pba.cbox.CBuniform_minimum(x)¶

pyuncertainnumber.pba.cbox.CBuniform_maximum(x)¶

pyuncertainnumber.pba.cbox.CBuniform(x)¶

pyuncertainnumber.pba.cbox.CBnonparametric(x)¶

pyuncertainnumber.pba.cbox.CBnormal_meandifference(x1, x2)¶

pyuncertainnumber.pba.cbox.CBnonparametric_deconvolution(x, error)¶

pyuncertainnumber.pba.cbox.named_cbox¶

pyuncertainnumber.pba.cbox.named_nextvalue¶