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## 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 < ...
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 < ...
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`.

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