ConditionalRandomVector¶

class ConditionalRandomVector(*args)¶

Conditional random vector.

Helper class for defining the random vector $\vect{X}$ such that $\vect{X}|\vect{\Theta}$ follows the distribution $\mathcal{L}_{\vect{X}|\vect{\Theta}}$ , with $\vect{\Theta}$ a random vector of dimension the dimension of $\vect{\Theta}$ .

Available constructors:: ConditionalRandomVector(conditionedDist, randomParameters)

Parameters

conditionedDistDistribution, the distribution of $\vect{X}|\vect{\Theta}$ , whose parameters will be overwritten by $\vect{\Theta}$ .
randomParametersRandomVector, the random parameters $\vect{\Theta}$ of the conditionedDist distribution.

Notes

Its probability density function is defined as:

$f_{\vect{X}}(\vect{x}) = \int f_{\vect{X}|\vect{\Theta}=\vect{\theta}}(\vect{x}|\vect{\theta}) f_{\vect{\Theta}}(\vect{\theta})\di{\vect{\theta}}$

with $f_{\vect{X}|\vect{\Theta}=\vect{\theta}}$ the PDF of the distribution of $\vect{X}|\vect{\Theta}$ , where $\vect{\Theta}$ has been replaced by $\vect{\theta}$ , $f_{\vect{\Theta}}$ the PDF of $\vect{\Theta}$ .

Note that there exist other (quasi) equivalent modellings using a combination of the classes ConditionalDistribution and RandomVector (see the Use Cases Guide).

Examples

Create a random vector:

>>> import openturns as ot
>>> distXgivenT = ot.Exponential()
>>> distGamma = ot.Uniform(1.0, 2.0)
>>> distAlpha = ot.Uniform(0.0, 0.1)
>>> distTheta = ot.ComposedDistribution([distGamma, distAlpha])
>>> rvTheta = ot.RandomVector(distTheta)
>>> rvX = ot.ConditionalRandomVector(distXgivenT, rvTheta)

Draw a sample:

>>> sample = rvX.getSample(5)

Methods

`getAntecedent`()	Accessor to the antecedent RandomVector in case of a composite RandomVector.
`getClassName`()	Accessor to the object’s name.
`getCovariance`()	Accessor to the covariance of the RandomVector.
`getDescription`()	Accessor to the description of the RandomVector.
`getDimension`()	Accessor to the dimension of the RandomVector.
`getDistribution`()	Accessor to the distribution’s conditioned distribution parameter conditionedDistribution.
`getDomain`()	Accessor to the domain of the Event.
`getFunction`()	Accessor to the Function in case of a composite RandomVector.
`getId`()	Accessor to the object’s id.
`getMarginal`(*args)	Get the random vector corresponding to the $i^{th}$ marginal component(s).
`getMean`()	Accessor to the mean of the RandomVector.
`getName`()	Accessor to the object’s name.
`getOperator`()	Accessor to the comparaison operator of the Event.
`getParameter`()	Accessor to the parameter of the distribution.
`getParameterDescription`()	Accessor to the parameter description of the distribution.
`getProcess`()	Get the stochastic process.
`getRandomParameters`()	Accessor to the distribution’s random parameter randomParameters.
`getRealization`(*args)	Compute one realization of the RandomVector.
`getSample`(size)	Compute realizations of the RandomVector.
`getShadowedId`()	Accessor to the object’s shadowed id.
`getThreshold`()	Accessor to the threshold of the Event.
`getVisibility`()	Accessor to the object’s visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isComposite`()	Accessor to know if the RandomVector is a composite one.
`isEvent`()	Whether the random vector is an event.
`setDescription`(description)	Accessor to the description of the RandomVector.
`setName`(name)	Accessor to the object’s name.
`setParameter`(parameters)	Accessor to the parameter of the distribution.
`setShadowedId`(id)	Accessor to the object’s shadowed id.
`setVisibility`(visible)	Accessor to the object’s visibility state.

__init__(*args)¶: Initialize self. See help(type(self)) for accurate signature.

getAntecedent()¶

Accessor to the antecedent RandomVector in case of a composite RandomVector.

Returns

antecedentRandomVector: Antecedent RandomVector $\vect{X}$ in case of a CompositeRandomVector such as: $\vect{Y}=f(\vect{X})$ .

getClassName()¶

Accessor to the object’s name.

Returns

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

getCovariance()¶

Accessor to the covariance of the RandomVector.

Returns

covarianceCovarianceMatrix: Covariance of the considered UsualRandomVector.

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.5], [1.0, 1.5], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getCovariance())
[[ 1    0    ]
 [ 0    2.25 ]]

getDescription()¶

Accessor to the description of the RandomVector.

Returns

descriptionDescription: Describes the components of the RandomVector.

getDimension()¶

Accessor to the dimension of the RandomVector.

Returns

dimensionpositive int: Dimension of the RandomVector.

getDistribution()¶

Accessor to the distribution’s conditioned distribution parameter conditionedDistribution.

Returns

conditionedDistributionDistribution, the distribution of $\vect{X}|\vect{\Theta}=\vect{\theta}$ , where the parameters $\vect{\theta}$ are equal to the values used to generate the last realization of $\vect{X}$ .

getDomain()¶

Accessor to the domain of the Event.

Returns

domainDomain: Describes the domain of an event.

getFunction()¶

Accessor to the Function in case of a composite RandomVector.

Returns

functionFunction: Function used to define a CompositeRandomVector as the image through this function of the antecedent $\vect{X}$ : $\vect{Y}=f(\vect{X})$ .

getId()¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getMarginal(*args)¶

Get the random vector corresponding to the $i^{th}$ marginal component(s).

Parameters

iint or list of ints, $0\leq i < dim$: Indicates the component(s) concerned. $dim$ is the dimension of the RandomVector.

Returns

vectorRandomVector: RandomVector restricted to the concerned components.

Notes

Let’s note $\vect{Y}=\Tr{(Y_1,\dots,Y_n)}$ a random vector and $I \in [1,n]$ a set of indices. If $\vect{Y}$ is a UsualRandomVector, the subvector is defined by $\tilde{\vect{Y}}=\Tr{(Y_i)}_{i \in I}$ . If $\vect{Y}$ is a CompositeRandomVector, defined by $\vect{Y}=f(\vect{X})$ with $f=(f_1,\dots,f_n)$ , $f_i$ some scalar functions, the subvector is $\tilde{\vect{Y}}=(f_i(\vect{X}))_{i \in I}$ .

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.0], [1.0, 1.0], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getMarginal(1).getRealization())
[0.608202]
>>> print(randomVector.getMarginal(1).getDistribution())
Normal(mu = 0, sigma = 1)

getMean()¶

Accessor to the mean of the RandomVector.

Returns

meanPoint: Mean of the considered UsualRandomVector.

Examples

>>> import openturns as ot
>>> distribution = ot.Normal([0.0, 0.5], [1.0, 1.5], ot.CorrelationMatrix(2))
>>> randomVector = ot.RandomVector(distribution)
>>> ot.RandomGenerator.SetSeed(0)
>>> print(randomVector.getMean())
[0,0.5]

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getOperator()¶

Accessor to the comparaison operator of the Event.

Returns

operatorComparisonOperator: Comparaison operator used to define the RandomVector.

getParameter()¶

Accessor to the parameter of the distribution.

Returns

parameterPoint: Parameter values.

getParameterDescription()¶

Accessor to the parameter description of the distribution.

Returns

descriptionDescription: Parameter names.

getProcess()¶

Get the stochastic process.

Returns

processProcess: Stochastic process used to define the RandomVector.

getRandomParameters()¶

Accessor to the distribution’s random parameter randomParameters.

Returns

randomParametersRandomVector, the random parameters $\vect{\Theta}$ .

getRealization(*args)¶

Compute one realization of the RandomVector.

Returns

aRealizationPoint: Sequence of values randomly determined from the RandomVector definition. In the case of an event: one realization of the event (considered as a Bernoulli variable) which is a boolean value (1 for the realization of the event and 0 else).

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