umontreal.iro.lecuyer.stochprocess
Class BrownianMotionPCAEqualSteps

java.lang.Object
  umontreal.iro.lecuyer.stochprocess.StochasticProcess
      umontreal.iro.lecuyer.stochprocess.BrownianMotion
          umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

public class BrownianMotionPCAEqualSteps
extends BrownianMotion

Same as BrownianMotionPCA, but uses a trick to speed up the calculation when the time steps are equidistant.

Constructor Summary

BrownianMotionPCAEqualSteps(double x0, double mu, double sigma, NormalGen gen)
Constructs a new BrownianMotionPCAEqualSteps.

BrownianMotionPCAEqualSteps(double x0, double mu, double sigma, RandomStream stream)
Constructs a new BrownianMotionPCAEqualSteps.

Method Summary

double[]	generatePath() Generates, returns, and saves the sample path {X(t0), X(t1),…, X(td)}.
double[]	generatePath(double[] QMCpointsBM) Same as generatePath(), but a vector of uniform random numbers must be provided to the method.
double[]	getSortedEigenvalues()
double	nextObservation() Generates and returns the next observation X(tj) of the stochastic process.
void	setObservationTimes(double[] t, int d) Sets the observation times of the process to a copy of T, with t0 = T[0] and td = T[d].
void	setObservationTimes(double dt, int d) Sets equidistant observation times at tj = jδ, for j = 0,..., d, and delta = δ.

Methods inherited from class umontreal.iro.lecuyer.stochprocess.BrownianMotion

generatePath, getGen, getMu, getSigma, getStream, nextObservation, nextObservation, setParams, setStream

Methods inherited from class umontreal.iro.lecuyer.stochprocess.StochasticProcess

getArrayMappingCounterToIndex, getCurrentObservation, getCurrentObservationIndex, getNbObservationTimes, getObservation, getObservationTimes, getPath, getSubpath, getX0, hasNextObservation, resetStartProcess, setX0

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BrownianMotionPCAEqualSteps

public BrownianMotionPCAEqualSteps(double x0,
                                   double mu,
                                   double sigma,
                                   RandomStream stream)

Constructs a new BrownianMotionPCAEqualSteps.

BrownianMotionPCAEqualSteps

public BrownianMotionPCAEqualSteps(double x0,
                                   double mu,
                                   double sigma,
                                   NormalGen gen)

Constructs a new BrownianMotionPCAEqualSteps.

Method Detail

nextObservation

public double nextObservation()

Description copied from class: StochasticProcess

Generates and returns the next observation X(t_j) of the stochastic process. The processes are usually sampled sequentially, i.e. if the last observation generated was for time t_j-1, the next observation returned will be for time t_j. In some cases, subclasses extending this abstract class may use non-sequential sampling algorithms (such as bridge sampling). The order of generation of the t_j's is then specified by the subclass. All the processes generated using principal components analysis (PCA) do not have this method.

Overrides:

nextObservation in class BrownianMotion

generatePath

public double[] generatePath()

Description copied from class: StochasticProcess

Generates, returns, and saves the sample path {X(t₀), X(t₁),…, X(t_d)}. It can then be accessed via getPath, getSubpath, or getObservation. The generation method depends on the process type.

Overrides:

generatePath in class BrownianMotion

generatePath

public double[] generatePath(double[] QMCpointsBM)

Description copied from class: BrownianMotion

Same as generatePath(), but a vector of uniform random numbers must be provided to the method. These uniform random numbers are used to generate the path.

Overrides:

generatePath in class BrownianMotion

setObservationTimes

public void setObservationTimes(double[] t,
                                int d)

Description copied from class: StochasticProcess

Sets the observation times of the process to a copy of T, with t₀ = T[0] and t_d = T[d]. The size of T must be d + 1.

Overrides:

setObservationTimes in class StochasticProcess

setObservationTimes

public void setObservationTimes(double dt,
                                int d)

Description copied from class: StochasticProcess

Sets equidistant observation times at t_j = jδ, for j = 0,..., d, and delta = δ.

Overrides:

setObservationTimes in class StochasticProcess

getSortedEigenvalues

public double[] getSortedEigenvalues()