Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess. More...

Inheritance diagram for umontreal.ssj.stochprocess.InverseGaussianProcessPCA:

Public Member Functions
	InverseGaussianProcessPCA (double s0, double delta, double gamma, RandomStream stream)
	Constructs a new InverseGaussianProcessPCA.
double[]	generatePath ()
	Generates, returns, and saves the sample path \(\{X(t_0), X(t_1), \dots, X(t_d)\}\).
double[]	generatePath (double[] uniforms01)
	Instead of using the internal stream to generate the path, uses an array of uniforms \(U[0,1)\).
double	nextObservation ()
	Not implementable for PCA.
void	setObservationTimes (double t[], int d)
	Sets the observation times of both the.
RandomStream	getStream ()
	Returns the random stream of the underlying generator.
void	setStream (RandomStream stream)
	Resets the random stream of the underlying generator to stream.
void	setBrownianMotionPCA (BrownianMotionPCA bmPCA)
	Sets the brownian motion PCA.
BrownianMotion	getBrownianMotionPCA ()
	Returns the BrownianMotionPCA.
Public Member Functions inherited from umontreal.ssj.stochprocess.InverseGaussianProcess
	InverseGaussianProcess (double s0, double delta, double gamma, RandomStream stream)
	Constructs a new InverseGaussianProcess.
double[]	generatePath (double[] uniforms01, double[] uniforms01b)
	This method does not work for this class, but will be useful for the subclasses that require two streams.
void	setParams (double delta, double gamma)
	Sets the parameters.
double	getDelta ()
	Returns \(\delta\).
double	getGamma ()
	Returns \(\gamma\).
double	getAnalyticAverage (double time)
	Returns the analytic average which is \(\delta t/ \gamma\), with.
double	getAnalyticVariance (double time)
	Returns the analytic variance which is \((\delta t)^2\), with.
int	getNumberOfRandomStreams ()
	Returns the number of random streams of this process.
Public Member Functions inherited from umontreal.ssj.stochprocess.StochasticProcess
void	setObservationTimes (double delta, int d)
	Sets equidistant observation times at \(t_j = j\delta\), for.
double[]	getObservationTimes ()
	Returns a reference to the array that contains the observation times.
int	getNumObservationTimes ()
	Returns the number \(d\) of observation times, excluding the time \(t_0\).
double[]	generatePath (RandomStream stream)
	Same as generatePath(), but first resets the stream to stream.
double[]	getPath ()
	Returns a reference to the last generated sample path \(\{X(t_0), ... , X(t_d)\}\).
void	getSubpath (double[] subpath, int[] pathIndices)
	Returns in subpath the values of the process at a subset of the observation times, specified as the times \(t_j\) whose indices.
double	getObservation (int j)
	Returns \(X(t_j)\) from the current sample path.
void	resetStartProcess ()
	Resets the observation counter to its initial value \(j=0\), so that the current observation \(X(t_j)\) becomes \(X(t_0)\).
boolean	hasNextObservation ()
	Returns true if \(j<d\), where \(j\) is the number of observations of the current sample path generated since the last call to resetStartProcess.
int	getCurrentObservationIndex ()
	Returns the value of the index \(j\) corresponding to the time.
double	getCurrentObservation ()
	Returns the value of the last generated observation \(X(t_j)\).
double	getX0 ()
	Returns the initial value \(X(t_0)\) for this process.
void	setX0 (double s0)
	Sets the initial value \(X(t_0)\) for this process to s0, and reinitializes.
int[]	getArrayMappingCounterToIndex ()
	Returns a reference to an array that maps an integer \(k\) to \(i_k\), the index of the observation \(S(t_{i_k})\) corresponding to the.

Detailed Description

Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess.

The PCA decomposition of a

BrownianMotionPCA with a covariance matrix identical to the one of our InverseGaussianProcess is used to generate the path of our InverseGaussianProcess [135] . Such a path is a perfectly random path and it is hoped that it will provide reduction in the simulation variance when using quasi-Monte Carlo.

The method nextObservation() cannot be used with PCA decompositions since the whole path must be generated at once.

Definition at line 46 of file InverseGaussianProcessPCA.java.

Constructor & Destructor Documentation

◆ InverseGaussianProcessPCA()

umontreal.ssj.stochprocess.InverseGaussianProcessPCA.InverseGaussianProcessPCA	(	double	s0,
		double	delta,
		double	gamma,
		RandomStream	stream )

Constructs a new InverseGaussianProcessPCA.

The initial value s0 will be overridden by \(t[0]\) when the observation times are set.

Definition at line 54 of file InverseGaussianProcessPCA.java.

Member Function Documentation

◆ generatePath() [1/2]

double[] umontreal.ssj.stochprocess.InverseGaussianProcessPCA.generatePath ( )

Generates, returns, and saves the sample path \(\{X(t_0), X(t_1), \dots, X(t_d)\}\).

It can then be accessed via getPath, getSubpath, or getObservation. The generation method depends on the process type.

Reimplemented from umontreal.ssj.stochprocess.InverseGaussianProcess.

Definition at line 60 of file InverseGaussianProcessPCA.java.

◆ generatePath() [2/2]

double[] umontreal.ssj.stochprocess.InverseGaussianProcessPCA.generatePath ( double[] uniforms01 )

Instead of using the internal stream to generate the path, uses an array of uniforms \(U[0,1)\).

The length of the array should be equal to the length of the number of periods in the observation times. This method is useful for NormalInverseGaussianProcess.

Reimplemented from umontreal.ssj.stochprocess.InverseGaussianProcess.

Definition at line 84 of file InverseGaussianProcessPCA.java.