SSJ
V. labo.

umontreal.iro.lecuyer.stochprocess
Class GammaProcess

java.lang.Object
  extended by umontreal.iro.lecuyer.stochprocess.StochasticProcess
      extended by umontreal.iro.lecuyer.stochprocess.GammaProcess
Direct Known Subclasses:
GammaProcessBridge, GammaProcessPCA

public class GammaProcess
extends StochasticProcess

GammaProcess

This class represents a gamma process[#!fMAD98a!#, page 82] {S(t) = G(t;μ, ν) : t≥0} with mean parameter μ and variance parameter ν. It is a continuous-time process with stationary, independent gamma increments such that for any Δt > 0,

S(t + Δt) = S(t) + X,

where X is a random variate from the gamma distribution Gamma (μ2Δt/ν, μ/ν).

In this class, the gamma process is sampled sequentially using equation.


Constructor Summary
GammaProcess(double s0, double mu, double nu, GammaGen Ggen)
          .
GammaProcess(double s0, double mu, double nu, RandomStream stream)
          .
 
Method Summary
 double[] generatePath()
          .
 double[] generatePath(double[] uniform01)
          .
 double getMu()
          .
 double getNu()
          .
 RandomStream getStream()
          .
 double nextObservation()
          Generates and returns the next observation X(tj) of the stochastic process.
 double nextObservation(double nextT)
          .
 void setParams(double s0, double mu, double nu)
          .
 void setStream(RandomStream stream)
          .
 
Methods inherited from class umontreal.iro.lecuyer.stochprocess.StochasticProcess
generatePath, getArrayMappingCounterToIndex, getCurrentObservation, getCurrentObservationIndex, getNbObservationTimes, getObservation, getObservationTimes, getPath, getSubpath, getX0, hasNextObservation, resetStartProcess, setObservationTimes, setObservationTimes, setX0
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

GammaProcess

public GammaProcess(double s0,
                    double mu,
                    double nu,
                    RandomStream stream)
. \begin{tabb}Constructs a new \texttt{GammaProcess} with parameters $\mu =
 \textt...
 ...ma variates $X$\ in are generated by inversion
 using \texttt{stream}.
 \end{tabb}


GammaProcess

public GammaProcess(double s0,
                    double mu,
                    double nu,
                    GammaGen Ggen)
. \begin{tabb}Constructs a new \texttt{GammaProcess} with parameters $\mu =
 \textt...
 ...orces the generator
 to use the correct parameters (as defined above).
 \end{tabb}

Method Detail

nextObservation

public double nextObservation()
Description copied from class: StochasticProcess
Generates and returns the next observation X(tj) of the stochastic process. The processes are usually sampled sequentially, i.e. if the last observation generated was for time tj-1, the next observation returned will be for time tj. In some cases, subclasses extending this abstract class may use non-sequential sampling algorithms (such as bridge sampling). The order of generation of the tj'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 StochasticProcess

nextObservation

public double nextObservation(double nextT)
. \begin{tabb}Generates and returns the next observation at time $t_{j+1} =
 \textt...
 ...The user must make sure that
 the $t_{j+1}$\ supplied is $\geq t_{j}$.
 \end{tabb}


generatePath

public double[] generatePath()
. \begin{tabb}
 Generates, returns and saves the path
 $\{X(t_{0}), X(t_{1}), \ldots...
 ...externalclass{umontreal.iro.lecuyer.randvar}{GammaGen} \texttt{Ggen}.
 \end{tabb}

Specified by:
generatePath in class StochasticProcess

generatePath

public double[] generatePath(double[] uniform01)
. \begin{tabb}Generates, returns and saves the path $
 \{X(t_{0}), X(t_{1}), \ldots...
 ...by inversion. The vector \texttt{uniform01} must be of
 dimension $d$.
 \end{tabb}


setParams

public void setParams(double s0,
                      double mu,
                      double nu)
. \begin{tabb}Sets the parameters $S(t_{0}) = \texttt{s0}$,
 $\mu = \texttt{mu}$\ a...
 ...quantities stored internally,
 which may be slow if called repeatedly.
 \end{tabb}


getMu

public double getMu()
. \begin{tabb}
 Returns the value of the parameter $\mu$.
 \end{tabb}


getNu

public double getNu()
. \begin{tabb}
 Returns the value of the parameter $\nu$.
 \end{tabb}


setStream

public void setStream(RandomStream stream)
. \begin{tabb}
 Resets the \externalclass{umontreal.iro.lecuyer.rng}{RandomStream}
 ...
 ...nalclass{umontreal.iro.lecuyer.randvar}{GammaGen}
 to \texttt{stream}.
 \end{tabb}

Specified by:
setStream in class StochasticProcess

getStream

public RandomStream getStream()
. \begin{tabb}
 Returns the \externalclass{umontreal.iro.lecuyer.rng}{RandomStream} \texttt{stream}.
 \end{tabb}

Specified by:
getStream in class StochasticProcess

SSJ
V. labo.

To submit a bug or ask questions, send an e-mail to Pierre L'Ecuyer.