Uses of Interface
net.finmath.montecarlo.automaticdifferentiation.RandomVariableDifferentiable
Package
Description
Provides classes adding automatic differentiation capabilities to objects relying on RandomVariable objects.
Provides the implementation of backward automatic differentiation.
Provides the implementation of forward automatic differentiation.
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Uses of RandomVariableDifferentiable in net.finmath.montecarlo.automaticdifferentiation
Modifier and TypeMethodDescriptionAbstractRandomVariableDifferentiableFactory.createRandomVariable
(double value) abstract RandomVariableDifferentiable
AbstractRandomVariableDifferentiableFactory.createRandomVariable
(double time, double value) abstract RandomVariableDifferentiable
AbstractRandomVariableDifferentiableFactory.createRandomVariable
(double time, double[] values) RandomVariableDifferentiableFactory.createRandomVariable
(double value) Create a (deterministic) random variable from a constant.RandomVariableDifferentiableFactory.createRandomVariable
(double time, double value) Create a (deterministic) random variable form a constant using a specific filtration time.RandomVariableDifferentiableFactory.createRandomVariable
(double time, double[] values) Create a random variable form an array using a specific filtration time.default RandomVariableDifferentiable
RandomVariableDifferentiable.getCloneIndependent()
Returns a clone of this differentiable random variable with a new ID. -
Uses of RandomVariableDifferentiable in net.finmath.montecarlo.automaticdifferentiation.backward
Modifier and TypeClassDescriptionclass
Implementation ofRandomVariableDifferentiable
using the backward algorithmic differentiation (adjoint algorithmic differentiation, AAD).Modifier and TypeMethodDescriptionRandomVariableDifferentiableAADFactory.createRandomVariable
(double time, double value) RandomVariableDifferentiableAADFactory.createRandomVariable
(double time, double[] values) RandomVariableDifferentiableAAD.getCloneIndependent()
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Uses of RandomVariableDifferentiable in net.finmath.montecarlo.automaticdifferentiation.forward
Modifier and TypeClassDescriptionclass
Implementation ofRandomVariableDifferentiable
using the forward algorithmic differentiation (AD).Modifier and TypeMethodDescriptionRandomVariableDifferentiableADFactory.createRandomVariable
(double time, double value) RandomVariableDifferentiableADFactory.createRandomVariable
(double time, double[] values)