Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess.
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Inheritance diagram for InverseGaussianProcessPCA:
Collaboration diagram for InverseGaussianProcessPCA:
Public Member Functions | |
| InverseGaussianProcessPCA (double s0, double delta, double gamma, RandomStream stream) | |
Constructs a new InverseGaussianProcessPCA. More... | |
| double [] | generatePath () |
| double [] | generatePath (double[] uniforms01) |
| Instead of using the internal stream to generate the path, uses an array of uniforms \(U[0,1)\). More... | |
| double | nextObservation () |
| Not implementable for PCA. | |
| void | setObservationTimes (double t[], int d) |
| Sets the observation times of both the InverseGaussianProcessPCA and the inner BrownianMotionPCA. | |
| RandomStream | getStream () |
| void | setStream (RandomStream stream) |
| void | setBrownianMotionPCA (BrownianMotionPCA bmPCA) |
| Sets the brownian motion PCA. More... | |
| BrownianMotion | getBrownianMotionPCA () |
| Returns the BrownianMotionPCA. | |
 Public Member Functions inherited from InverseGaussianProcess | |
| InverseGaussianProcess (double s0, double delta, double gamma, RandomStream stream) | |
Constructs a new InverseGaussianProcess. More... | |
| double [] | generatePath () |
| double [] | generatePath (double[] uniforms01) |
| Instead of using the internal stream to generate the path, uses an array of uniforms \(U[0,1)\). More... | |
| 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. | |
| double | nextObservation () |
| 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 \(t=\) time. | |
| double | getAnalyticVariance (double time) |
Returns the analytic variance which is \((\delta t)^2\), with \(t=\) time. | |
| RandomStream | getStream () |
| void | setStream (RandomStream stream) |
| int | getNumberOfRandomStreams () |
| Returns the number of random streams of this process. More... | |
| Public Member Functions inherited from StochasticProcess | |
| void | setObservationTimes (double[] T, int d) |
Sets the observation times of the process to a copy of T, with. More... | |
| void | setObservationTimes (double delta, int d) |
| Sets equidistant observation times at \(t_j = j\delta\), for. More... | |
| double [] | getObservationTimes () |
| Returns a reference to the array that contains the observation times. More... | |
| int | getNumObservationTimes () |
| Returns the number \(d\) of observation times, excluding the time \(t_0\). | |
| abstract double [] | generatePath () |
| Generates, returns, and saves the sample path \(\{X(t_0), X(t_1), \dots, X(t_d)\}\). More... | |
| 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 \(\{X(t_0), ... , X(t_d)\}\). More... | |
| 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. More... | |
| double | getObservation (int j) |
| Returns \(X(t_j)\) from the current sample path. More... | |
| void | resetStartProcess () |
| Resets the observation counter to its initial value \(j=0\), so that the current observation \(X(t_j)\) becomes \(X(t_0)\). More... | |
| 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. More... | |
| double | nextObservation () |
| Generates and returns the next observation \(X(t_j)\) of the stochastic process. More... | |
| int | getCurrentObservationIndex () |
| Returns the value of the index \(j\) corresponding to the time. More... | |
| 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. | |
| abstract void | setStream (RandomStream stream) |
Resets the random stream of the underlying generator to stream. | |
| abstract RandomStream | getStream () |
| Returns the random stream of the underlying generator. | |
| 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 \(k\)-th observation to be generated for a sample path of this process. More... | |
Protected Attributes | |
| BrownianMotionPCA | bmPCA |
| Protected Attributes inherited from InverseGaussianProcess | |
| RandomStream | stream |
| double | delta |
| double | gamma |
| double | deltaOverGamma |
| double | deltaSquare |
| double [] | imu |
| double [] | ilam |
| Protected Attributes inherited from StochasticProcess | |
| boolean | observationTimesSet = false |
| double | x0 = 0.0 |
| int | d = -1 |
| int | observationIndex = 0 |
| int | observationCounter = 0 |
| double [] | t |
| double [] | path |
| int [] | observationIndexFromCounter |
Additional Inherited Members | |
| Protected Member Functions inherited from InverseGaussianProcess | |
| void | init () |
| Protected Member Functions inherited from StochasticProcess | |
| void | init () |
| Package Attributes inherited from InverseGaussianProcess | |
| int | numberOfRandomStreams |
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 [140] . 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.
Constructor & Destructor Documentation
◆ 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.
Member Function Documentation
◆ generatePath()
| double [] 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.
◆ setBrownianMotionPCA()
| void setBrownianMotionPCA | ( | BrownianMotionPCA | bmPCA | ) |
Sets the brownian motion PCA.
The observation times will be overriden when the method observationTimes() is called on the InverseGaussianProcessPCA.
The documentation for this class was generated from the following file:
- InverseGaussianProcessPCA.java
