umontreal.iro.lecuyer.stochprocess
Class GammaProcessSymmetricalBridge

java.lang.Object
  umontreal.iro.lecuyer.stochprocess.StochasticProcess
      umontreal.iro.lecuyer.stochprocess.GammaProcess
          umontreal.iro.lecuyer.stochprocess.GammaProcessBridge
              umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

public class GammaProcessSymmetricalBridge
extends GammaProcessBridge

This class differs from GammaProcessBridge only in that it requires the number of interval of the path to be a power of 2 and of equal size. It is then possible to generate the bridge process using a special implementation of the beta random variate generator (using the symmetrical beta distribution) that is much faster (HOW MUCH? QUANTIFY!) than the general case. Note that when the method setObservationTimes is called, the equality of the size of the time steps is verified. To allow for differences due to floating point errors, time steps are considered to be equal if their relative difference is less than 10^-15.

Constructor Summary

GammaProcessSymmetricalBridge(double s0, double mu, double nu, GammaGen Ggen, BetaSymmetricalGen BSgen)
Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessSymmetricalBridge(double s0, double mu, double nu, RandomStream stream)
Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

Method Summary

double[]	generatePath() Generates, returns and saves the path {X(t0), X(t1),…, X(td)}.
double[]	generatePath(double[] uniform01) Generates, returns and saves the path {X(t0), X(t1),…, X(td)}.
double	nextObservation() Generates and returns the next observation X(tj) of the stochastic process.
double	nextObservation(double nextT) Generates and returns the next observation at time tj+1 = nextTime, using the previous observation time tj defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(tj).

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

resetStartProcess, setStream

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

getMu, getNu, getStream, setParams

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

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

Methods inherited from class java.lang.Object

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

Constructor Detail

GammaProcessSymmetricalBridge

public GammaProcessSymmetricalBridge(double s0,
                                     double mu,
                                     double nu,
                                     RandomStream stream)

Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0. The random variables are created using the RandomStream stream. Note that the same RandomStream stream is used for the GammaGen and for the BetaSymmetricalGen inluded in this class.

GammaProcessSymmetricalBridge

public GammaProcessSymmetricalBridge(double s0,
                                     double mu,
                                     double nu,
                                     GammaGen Ggen,
                                     BetaSymmetricalGen BSgen)

Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0. Note that the RandomStream included in the BetaSymmetricalGen is sets to the one included in the GammaGen to avoid confusion. This RandomStream is then used to generate all the random variables.

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 GammaProcessBridge

nextObservation

public double nextObservation(double nextT)

Description copied from class: GammaProcess

Generates and returns the next observation at time t_j+1 = nextTime, using the previous observation time t_j defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(t_j). Warning: This method will reset the observations time t_j+1 for this process to nextT. The user must make sure that the t_j+1 supplied is >= t_j.

Overrides:

nextObservation in class GammaProcessBridge

generatePath

public double[] generatePath()

Description copied from class: GammaProcess

Generates, returns and saves the path {X(t₀), X(t₁),…, X(t_d)}. The gamma variates X in are generated using the RandomStream stream or the RandomStream included in the GammaGen Ggen.

Overrides:

generatePath in class GammaProcessBridge

generatePath

public double[] generatePath(double[] uniform01)

Description copied from class: GammaProcess

Generates, returns and saves the path {X(t₀), X(t₁),…, X(t_d)}. This method does not use the RandomStream stream nor the GammaGen Ggen. It uses the vector of uniform random numbers U(0, 1) provided by the user and generates the path by inversion. The vector uniform01 must be of dimension d.

Overrides:

generatePath in class GammaProcessBridge