DistributionFactory

class DistributionFactory(*args)

Base class for probability distribution factories.

Methods

GetByName(name)	Instantiate a distribution factory.
GetContinuousMultiVariateFactories()	Accessor to the list of continuous multivariate factories.
GetContinuousUniVariateFactories()	Accessor to the list of continuous univariate factories.
GetDiscreteMultiVariateFactories()	Accessor to the list of discrete multivariate factories.
GetDiscreteUniVariateFactories()	Accessor to the list of discrete univariate factories.
GetMultiVariateFactories()	Accessor to the list of multivariate factories.
GetUniVariateFactories()	Accessor to the list of univariate factories.
build(*args)	Build the distribution.
buildEstimator(*args)	Build the distribution and the parameter distribution.
getClassName()	Accessor to the object's name.
getId()	Accessor to the object's id.
getImplementation()	Accessor to the underlying implementation.
getKnownParameterIndices()	Accessor to the known parameters indices.
getKnownParameterValues()	Accessor to the known parameters values.
getName()	Accessor to the object's name.
setKnownParameter(values, positions)	Accessor to the known parameters.
setName(name)	Accessor to the object's name.

Notes

This class generally describes the factory mechanism of each OpenTURNS distribution. Refer to Parametric Estimation for information on the specific estimators used for each distribution.

__init__(*args)

static GetByName(name)

Instantiate a distribution factory.

Parameters:

namestr

Factory name

Returns:

factoryDistributionFactory

An instance of the desired class.

static GetContinuousMultiVariateFactories()

Accessor to the list of continuous multivariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid continuous multivariate factories.

static GetContinuousUniVariateFactories()

Accessor to the list of continuous univariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid continuous univariate factories.

static GetDiscreteMultiVariateFactories()

Accessor to the list of discrete multivariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid discrete multivariate factories.

static GetDiscreteUniVariateFactories()

Accessor to the list of discrete univariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid discrete univariate factories.

static GetMultiVariateFactories()

Accessor to the list of multivariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid multivariate factories.

static GetUniVariateFactories()

Accessor to the list of univariate factories.

Returns:

listFactoriescollection of DistributionFactory

All the valid univariate factories.

build(*args)

Build the distribution.

Available usages:

build()

build(sample)

build(param)

Parameters:

sample2-d sequence of float

Data.

paramsequence of float

The parameters of the distribution.

Returns:

distDistribution

The estimated distribution.

In the first usage, the default native distribution is built.

buildEstimator(*args)

Build the distribution and the parameter distribution.

Parameters:

sample2-d sequence of float

Data.

parametersDistributionParameters

Optional, the parametrization.

Returns:

resDistDistributionFactoryResult

The results.

Notes

According to the way the native parameters of the distribution are estimated, the parameters distribution differs:

• Moments method: the asymptotic parameters distribution is normal and estimated by Bootstrap on the initial data;

• Maximum likelihood method with a regular model: the asymptotic parameters distribution is normal and its covariance matrix is the inverse Fisher information matrix;

• Other methods: the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting (see KernelSmoothing).

If another set of parameters is specified, the native parameters distribution is first estimated and the new distribution is determined from it:

• if the native parameters distribution is normal and the transformation regular at the estimated parameters values: the asymptotic parameters distribution is normal and its covariance matrix determined from the inverse Fisher information matrix of the native parameters and the transformation;

• in the other cases, the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:

implImplementation

A copy of the underlying implementation object.

getKnownParameterIndices()

Accessor to the known parameters indices.

Returns:

indicesIndices

Indices of the known parameters.

getKnownParameterValues()

Accessor to the known parameters values.

Returns:

valuesPoint

Values of known parameters.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

setKnownParameter(values, positions)

Accessor to the known parameters.

Parameters:

valuessequence of float

Values of known parameters.

positionssequence of int

Indices of known parameters.

Examples

When a subset of the parameter vector is known, the other parameters only have to be estimated from data.

In the following example, we consider a sample and want to fit a Beta distribution. We assume that the and parameters are known beforehand. In this case, we set the third parameter (at index 2) to -1 and the fourth parameter (at index 3) to 1.

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Beta(2.3, 2.2, -1.0, 1.0)
>>> sample = distribution.getSample(10)
>>> factory = ot.BetaFactory()
>>> # set (a,b) out of (r, t, a, b)
>>> factory.setKnownParameter([-1.0, 1.0], [2, 3])
>>> inf_distribution = factory.build(sample)

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

Estimate a multivariate distribution

Estimate a multivariate distribution