MaximumLikelihoodFactory¶

class MaximumLikelihoodFactory(*args)¶

Maximum likelihood factory.

The maximum likelihood method is detailed in Maximum Likelihood Principle.

Parameters:

distributionDistribution: The parametric distribution $p_{\vect{\theta}}$ .

See also

DistributionFactory

Notes

This class implements the generic maximum likelihood estimation.

Let us denote $(\vect{x}_1, \dots, \vect{x}_n)$ the sample, $p_{\vect{\theta}}$ the density of the parametric distribution we want to fit to the sample, with the parameter vector $\vect{\theta} \in \Theta \in \Rset^p$ .

The likelihood of the sample according to $p_{\vect{\theta}}$ is:

$L(\vect{x}_1, \dots, \vect{x}_n; \vect{\theta}) = \prod_{i=1}^n p_{\vect{\theta}}(\vect{x}_i)$

The log-likelihood is defined as:

$\ell(\vect{x}_1, \dots, \vect{x}_n; \vect{\theta}) = \sum_{i=1}^n \log p_{\vect{\theta}}(\vect{x}_i)$

The estimator of $\vect{\theta}$ maximizes the log-likelihood:

$\hat{\vect{\theta}} = \argmax_{\vect{\theta} \in \Theta} \log \ell (\vect{x}_1, \dots, \vect{x}_n; \vect{\theta})$

Examples

In the following example, we estimate the parameters of a Normal distribution with maximum likelihood estimation.

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal(0.9, 1.7)
>>> sample = distribution.getSample(10)
>>> factory = ot.MaximumLikelihoodFactory(ot.Normal())
>>> inf_distribution = factory.build(sample)

Methods

`BuildEstimator`(factory, sample[, isRegular])	Estimate the parameters and the asymptotic distribution.
`BuildGaussianEstimator`(distribution, sample)	Compute the asymptotic distribution of the parameters.
`build`(*args)	Build the distribution.
`buildEstimator`(*args)	Build the distribution and the parameter distribution.
`getBootstrapSize`()	Accessor to the bootstrap size.
`getClassName`()	Accessor to the object's name.
`getId`()	Accessor to the object's id.
`getKnownParameterIndices`()	Accessor to the known parameters indices.
`getKnownParameterValues`()	Accessor to the known parameters indices.
`getName`()	Accessor to the object's name.
`getOptimizationAlgorithm`()	Accessor to the optimization solver.
`getOptimizationBounds`()	Accessor to the optimization bounds.
`getShadowedId`()	Accessor to the object's shadowed id.
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`setBootstrapSize`(bootstrapSize)	Accessor to the bootstrap size.
`setKnownParameter`(values, positions)	Accessor to the known parameters.
`setName`(name)	Accessor to the object's name.
`setOptimizationAlgorithm`(solver)	Accessor to the optimization solver.
`setOptimizationBounds`(optimizationBounds)	Accessor to the optimization bounds.
`setOptimizationInequalityConstraint`(...)	Accessor to the optimization inequality constraint.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setVisibility`(visible)	Accessor to the object's visibility state.

__init__(*args)¶

static BuildEstimator(factory, sample, isRegular=False)¶

Estimate the parameters and the asymptotic distribution.

Parameters:

factoryDistributionFactory: Distribution factory to infer the data
sample2-d sequence of float: Data to infer
is_regularbool: Indicates whether the parametric distribution is regular.

Returns:

resultDistributionFactoryResult: Result class providing the estimate and the asymptotic distribution.

Notes

If the model is regular, the asymptotic distribution of the estimator is normal and we get it from the Delta method.

If the model is not regular, we use the Bootstrap method and the kernel smoothing method to get the asymptotic distribution of the estimator.

static BuildGaussianEstimator(distribution, sample)¶

Compute the asymptotic distribution of the parameters.

Parameters:

distributionDistribution: Parametric distribution.
sample2-d sequence of float: Data to infer.

Returns:

distributionNormal: Asymptotic normal distribution of $\hat{\vect{\theta}}$ .

Notes

We assume that the parametric model is regular: then, the asymptotic distribution of $\hat{\vect{\theta}}$ is normal.

build(*args)¶

Build the distribution.

Available usages:

build(sample)

build(param)

Parameters:

sample2-d sequence of float: Sample from which the distribution parameters are estimated.
paramCollection of PointWithDescription: A vector of parameters of the distribution.

Returns:

distDistribution: The built distribution.

buildEstimator(*args)¶

Build the distribution and the parameter distribution.

Parameters:

sample2-d sequence of float: Sample from which the distribution parameters are estimated.
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.

getBootstrapSize()¶

Accessor to the bootstrap size.

Returns:

sizeinteger: Size of the bootstrap.

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.

getKnownParameterIndices()¶

Accessor to the known parameters indices.

Returns:

indicesIndices: Indices of the known parameters.

getKnownParameterValues()¶

Accessor to the known parameters indices.

Returns:

valuesPoint: Values of known parameters.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getOptimizationAlgorithm()¶

Accessor to the optimization solver.

Returns:

solverOptimizationAlgorithm: The solver used for the optimization of the log-likelihood.

getOptimizationBounds()¶

Accessor to the optimization bounds.

Returns:

boundsInterval: The bounds used for the optimization of the log-likelihood.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool: True if the name is not empty and not the default one.

setBootstrapSize(bootstrapSize)¶

Accessor to the bootstrap size.

Parameters:

sizeinteger: Size of the bootstrap.

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 $a$ and $b$ 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.MaximumLikelihoodFactory(ot.Beta())
>>> # 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.

setOptimizationAlgorithm(solver)¶

Accessor to the optimization solver.

Parameters:

solverOptimizationAlgorithm: The solver used for the optimization of the log-likelihood.

setOptimizationBounds(optimizationBounds)¶

Accessor to the optimization bounds.

Parameters:

boundsInterval: The bounds used for the optimization of the log-likelihood.

setOptimizationInequalityConstraint(optimizationInequalityConstraint)¶

Accessor to the optimization inequality constraint.

Parameters:

inequalityConstraintFunction: The inequality constraint used for the optimization of the log-likelihood.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

Examples using the class¶

Fit a distribution by maximum likelihood

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Examples using the class¶