The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, umontreal.ssj.probdist.InverseGaussianDist. More...
Public Member Functions | |
| 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 Member Functions | |
| void | init () |
| Protected Member Functions inherited from StochasticProcess | |
| void | init () |
Protected Attributes | |
| 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 |
Package Attributes | |
| int | numberOfRandomStreams |
Detailed Description
The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, umontreal.ssj.probdist.InverseGaussianDist.
With parameters \(\delta\) and \(\gamma\), the time increments are given by umontreal.ssj.probdist.InverseGaussianDist \((\delta dt/\gamma, \delta^2 dt^2)\).
[We here use the inverse gaussian distribution parametrized with IGDist \((\mu,\lambda)\), where \(\mu=\delta/\gamma\) and \(\lambda=\delta^2\). If we instead used the parametrization \(IGDist^{\star}(\delta, \gamma)\), then the increment distribution of our process would have been written more simply as \(IGDist^{\star}(\delta dt, \gamma)\).]
The increments are generated by using the inversion of the cumulative distribution function. It therefore uses only one umontreal.ssj.rng.RandomStream. Subclasses of this class use different generating methods and some need two umontreal.ssj.rng.RandomStream ’s.
The initial value of this process is the initial observation time.
Constructor & Destructor Documentation
◆ InverseGaussianProcess()
| InverseGaussianProcess | ( | double | s0, |
| double | delta, | ||
| double | gamma, | ||
| RandomStream | stream | ||
| ) |
Constructs a new InverseGaussianProcess.
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 array should be of the length of the number of periods in the observation times. This method is useful for NormalInverseGaussianProcess.
◆ getNumberOfRandomStreams()
| int getNumberOfRandomStreams | ( | ) |
Returns the number of random streams of this process.
It is useful because some subclasses use different number of streams. It returns 1 for InverseGaussianProcess.
The documentation for this class was generated from the following file:
- InverseGaussianProcess.java
