SSJ
V. 2.5.

## Package umontreal.iro.lecuyer.stochprocess

This package provides classes to define stochastic processes {X(t), t >= 0}, and to simulate their sample paths at a finite number of (discrete) observation times t0 <= t1 <=  ...

See:
Description

Class Summary
BrownianMotion This class represents a Brownian motion process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
BrownianMotionBridge Represents a Brownian motion process {X(t) : t >= 0} sampled using the bridge sampling technique (see for example).
BrownianMotionPCA A Brownian motion process {X(t) : t >= 0} sampled using the principal component decomposition (PCA).
BrownianMotionPCAEqualSteps Same as BrownianMotionPCA, but uses a trick to speed up the calculation when the time steps are equidistant.
CIRProcess This class represents a CIR (Cox, Ingersoll, Ross) process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
CIRProcessEuler .
GammaProcess This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν.
GammaProcessBridge This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν, sampled using the gamma bridge method (see for example).
GammaProcessPCA Represents a gamma process sampled using the principal component analysis (PCA).
GammaProcessPCABridge Same as GammaProcessPCA, but the generated uniforms correspond to a bridge transformation of the BrownianMotionPCA instead of a sequential transformation.
GammaProcessPCASymmetricalBridge Same as GammaProcessPCABridge, but uses the fast inversion method for the symmetrical beta distribution, proposed by L'Ecuyer and Simard, to accelerate the generation of the beta random variables.
GammaProcessSymmetricalBridge 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.
GeometricBrownianMotion .
GeometricLevyProcess .
GeometricNormalInverseGaussianProcess .
GeometricVarianceGammaProcess This class represents a geometric variance gamma process S(t) (see).
InverseGaussianProcess The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, InverseGaussianDist.
InverseGaussianProcessBridge Samples the path by bridge sampling: first finding the process value at the final time and then the middle time, etc.
InverseGaussianProcessMSH Uses a faster generating method (MSH) than the simple inversion of the distribution function used by InverseGaussianProcess.
InverseGaussianProcessPCA Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess.
NormalInverseGaussianProcess This class represents a normal inverse gaussian process (NIG).
OrnsteinUhlenbeckProcess This class represents an Ornstein-Uhlenbeck process {X(t) : t >= 0}, sampled at times 0 = t0 < t1 < ...
OrnsteinUhlenbeckProcessEuler .
StochasticProcess Abstract base class for a stochastic process {X(t) : t >= 0} sampled (or observed) at a finite number of time points, 0 = t0 < t1 < ...
VarianceGammaProcess This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcessDiff This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.
VarianceGammaProcessDiffPCA Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of PCA type.
VarianceGammaProcessDiffPCABridge Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of the type PCABridge.
VarianceGammaProcessDiffPCASymmetricalBridge Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of the PCASymmetricalBridge type.

## Package umontreal.iro.lecuyer.stochprocess Description

This package provides classes to define stochastic processes {X(t), t >= 0}, and to simulate their sample paths at a finite number of (discrete) observation times t0 <= t1 <=  ...  <= td. The observation of the generated path is thus the vector (X(t0), X(t1),..., X(td)).

The observation times t0,..., td can be specified (or changed) after defining the process, with the method setObservationTimes. The random stream used to generate the sample path can also be changed, using setStream.

SSJ
V. 2.5.

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