Module net.finmath.lib
Package net.finmath.montecarlo

Class IndependentIncrementsFromICDF

java.lang.Object
net.finmath.montecarlo.IndependentIncrementsFromICDF
All Implemented Interfaces:
Serializable, IndependentIncrements
public class IndependentIncrementsFromICDF extends Object implements IndependentIncrements, Serializable
Implementation of a time-discrete n-dimensional sequence of independent increments W = (W1,...,Wn) form a given set of inverse cumulative distribution functions. Independent increments is a sequence of independent random variables index by the time index associated with the time discretization. At each time step the increment is a d-dimensional random variable \( Z(t_{i}) \), where d is numberOfFactors. where each component of \( Z_{j}(t_{i}) \) is given by \[ Z_{j}(t_{i}) = ICDF_{i,j}(U_{i,j}) \] for a sequence of independent uniform distributes random variables U_{i,j}. The inverse cumulative distribution functions \( ICDF_{i,j} \) are given by inverseCumulativeDistributionFunctions as the map \( i \mapsto ( j \mapsto ICDF_{i,j} ) \) (here i is the time index and j is the factor (component). Each \( U_{i,j} \) is samples using numberOfPaths. The class is immutable and thread safe. It uses lazy initialization.
Version:
1.6
Author:
Christian Fries
See Also:

  • Constructor Details

    • IndependentIncrementsFromICDF

      public IndependentIncrementsFromICDF(TimeDiscretization timeDiscretization, int numberOfFactors, int numberOfPaths, int seed, IntFunction<IntFunction<DoubleUnaryOperator>> inverseCumulativeDistributionFunctions, RandomVariableFactory randomVariableFactory)
      Construct the simulation of independent increments. Independent increments is a sequence of independent random variables index by the time index associated with the time discretization. At each time step the increment is a d-dimensional random variable \( Z(t_{i}) \), where d is numberOfFactors. where each component of \( Z_{j}(t_{i}) \) is given by \[ Z_{j}(t_{i}) = ICDF_{i,j}(U_{i,j}) \] for a sequence of independent uniform distributes random variables U_{i,j}. The inverse cumulative distribution functions \( ICDF_{i,j} \) are given by inverseCumulativeDistributionFunctions as the map \( i \mapsto ( j \mapsto ICDF_{i,j} ) \) (here i is the time index and j is the factor (component). Each \( U_{i,j} \) is samples using numberOfPaths. The constructor allows to set the factory to be used for the construction of random variables. This allows to generate increments represented by different implementations of the RandomVariable (e.g. the RandomVariableFromFloatArray internally using float representations).
      Parameters:
      timeDiscretization - The time discretization used for the increments.
      numberOfFactors - Number of factors.
      numberOfPaths - Number of paths to simulate.
      seed - The seed of the random number generator.
      inverseCumulativeDistributionFunctions - A map from the timeIndices to a map from the from the factors to the corresponding inverse cumulative distribution function.
      randomVariableFactory - Factory to be used to create random variable.

    • IndependentIncrementsFromICDF

      public IndependentIncrementsFromICDF(TimeDiscretization timeDiscretization, int numberOfFactors, int numberOfPaths, int seed, IntFunction<IntFunction<DoubleUnaryOperator>> inverseCumulativeDistributionFunctions)
      Construct the simulation of independet increments. The independent increments is a sequence of independent random variables index by the time index associated with the time discretization. At each time step the increment is a d-dimensional random variable \( Z(t_{i}) \), where d is numberOfFactors. where each component of \( Z_{j}(t_{i}) \) is given by \[ Z_{j}(t_{i}) = ICDF_{i,j}(U_{i,j}) \] for a sequence of independent uniform distributes random variables U_{i,j}. The inverse cumulative distribution functions \( ICDF_{i,j} \) are given by inverseCumulativeDistributionFunctions as the map \( i \mapsto ( j \mapsto ICDF_{i,j} ) \) (here i is the time index and j is the factor (component). Each \( U_{i,j} \) is samples using numberOfPaths.
      Parameters:
      timeDiscretization - The time discretization used for the increments.
      numberOfFactors - Number of factors.
      numberOfPaths - Number of paths to simulate.
      seed - The seed of the random number generator.
      inverseCumulativeDistributionFunctions - A map from the timeIndices to a map from the from the factors to the corresponding inverse cumulative distribution function.

  • Method Details

    • getCloneWithModifiedSeed

      public IndependentIncrements getCloneWithModifiedSeed(int seed)
      Description copied from interface: IndependentIncrements
      Return a new object implementing BrownianMotion having the same specifications as this object but a different seed for the random number generator. This method is useful if you like to make Monte-Carlo samplings by changing the seed.
      Specified by:
      getCloneWithModifiedSeed in interface IndependentIncrements
      Parameters:
      seed - New value for the seed.
      Returns:
      New object implementing BrownianMotion.

    • getCloneWithModifiedTimeDiscretization

      public IndependentIncrements getCloneWithModifiedTimeDiscretization(TimeDiscretization newTimeDiscretization)
      Description copied from interface: IndependentIncrements
      Return a new object implementing BrownianMotion having the same specifications as this object but a different time discretization.
      Specified by:
      getCloneWithModifiedTimeDiscretization in interface IndependentIncrements
      Parameters:
      newTimeDiscretization - New time discretization
      Returns:
      New object implementing BrownianMotion.

    • getIncrement

      public RandomVariable getIncrement(int timeIndex, int factor)
      Description copied from interface: IndependentIncrements
      Return the increment for a given timeIndex and given factor. The method returns the random variable Δ Xj(ti) := Xj(ti+1)-X(ti) for the given time index i and a given factor (index) j
      Specified by:
      getIncrement in interface IndependentIncrements
      Parameters:
      timeIndex - The time index (corresponding to the this class's time discretization)
      factor - The index of the factor (independent scalar increment)
      Returns:
      The factor (component) of the increments (a random variable)

    • getTimeDiscretization

      public TimeDiscretization getTimeDiscretization()
      Description copied from interface: IndependentIncrements
      Returns the time discretization used for this set of time-discrete Brownian increments.
      Specified by:
      getTimeDiscretization in interface IndependentIncrements
      Returns:
      The time discretization used for this set of time-discrete Brownian increments.

    • getNumberOfFactors

      public int getNumberOfFactors()
      Description copied from interface: IndependentIncrements
      Returns the number of factors.
      Specified by:
      getNumberOfFactors in interface IndependentIncrements
      Returns:
      The number of factors.

    • getNumberOfPaths

      public int getNumberOfPaths()
      Description copied from interface: IndependentIncrements
      Returns the number of paths.
      Specified by:
      getNumberOfPaths in interface IndependentIncrements
      Returns:
      The number of paths.

    • getRandomVariableForConstant

      public RandomVariable getRandomVariableForConstant(double value)
      Description copied from interface: IndependentIncrements
      Returns a random variable which is initialized to a constant, but has exactly the same number of paths or discretization points as the ones used by this BrownianMotion.
      Specified by:
      getRandomVariableForConstant in interface IndependentIncrements
      Parameters:
      value - The constant value to be used for initialized the random variable.
      Returns:
      A new random variable.

    • getSeed

      public int getSeed()
      Returns:
      Returns the seed.

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • equals

      public boolean equals(Object o)
      Overrides:
      equals in class Object

    • hashCode

      public int hashCode()
      Overrides:
      hashCode in class Object