# DistributionFactory¶

class DistributionFactory(*args)

Base class for probability distribution factories.

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.

Attributes: thisown The membership flag

Methods

 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(*args) Accessor to the underlying implementation. getName() Accessor to the object’s name. setName(name) Accessor to the object’s name.
__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

static GetContinuousMultiVariateFactories()

Accessor to the list of continuous multivariate factories.

Returns: listFactories : collection of DistributionFactory All the valid continuous multivariate factories.
static GetContinuousUniVariateFactories()

Accessor to the list of continuous univariate factories.

Returns: listFactories : collection of DistributionFactory All the valid continuous univariate factories.
static GetDiscreteMultiVariateFactories()

Accessor to the list of discrete multivariate factories.

Returns: listFactories : collection of DistributionFactory All the valid discrete multivariate factories.
static GetDiscreteUniVariateFactories()

Accessor to the list of discrete univariate factories.

Returns: listFactories : collection of DistributionFactory All the valid discrete univariate factories.
static GetMultiVariateFactories()

Accessor to the list of multivariate factories.

Returns: listFactories : collection of DistributionFactory All the valid multivariate factories.
static GetUniVariateFactories()

Accessor to the list of univariate factories.

Returns: listFactories : collection of DistributionFactory All the valid univariate factories.
build(*args)

Build the distribution.

Available usages:

build(sample)

build(param)

Parameters: sample : 2-d sequence of float Sample from which the distribution parameters are estimated. param : Collection of PointWithDescription A vector of parameters of the distribution. dist : Distribution The built distribution.
buildEstimator(*args)

Build the distribution and the parameter distribution.

Parameters: sample : 2-d sequence of float Sample from which the distribution parameters are estimated. parameters : DistributionParameters Optional, the parametrization. resDist : DistributionFactoryResult 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.

Examples

Create a sample from a Beta distribution:

>>> import openturns as ot
>>> sample = ot.Beta().getSample(10)
>>> ot.ResourceMap.SetAsUnsignedInteger('DistributionFactory-DefaultBootstrapSize', 100)


Fit a Beta distribution in the native parameters and create a DistributionFactory:

>>> fittedRes = ot.BetaFactory().buildEstimator(sample)


Fit a Beta distribution in the alternative parametrization :

>>> fittedRes2 = ot.BetaFactory().buildEstimator(sample, ot.BetaMuSigma())

getClassName()

Accessor to the object’s name.

Returns: class_name : str The object class name (object.__class__.__name__).
getId()

Accessor to the object’s id.

Returns: id : int Internal unique identifier.
getImplementation(*args)

Accessor to the underlying implementation.

Returns: impl : Implementation The implementation class.
getName()

Accessor to the object’s name.

Returns: name : str The name of the object.
setName(name)

Accessor to the object’s name.

Parameters: name : str The name of the object.
thisown

The membership flag