Uses a faster generating method (MSH) [180] than the simple inversion of the distribution function used by InverseGaussianProcess. More...
Inheritance diagram for InverseGaussianProcessMSH:
Collaboration diagram for InverseGaussianProcessMSH:
Classes | |
| class | NonRandomStream |
| NonRandomStream: Given a double array, this class will return those values as if it where a random stream. More... | |
Public Member Functions | |
| InverseGaussianProcessMSH (double s0, double delta, double gamma, RandomStream stream, RandomStream otherStream) | |
Constructs a new InverseGaussianProcessMSH. More... | |
| double [] | generatePath () |
| Generates the path. More... | |
| double [] | generatePath (double[] unifNorm, double[] unifOther) |
| Instead of using the internal streams to generate the path, uses two arrays of uniforms \(U[0,1)\). More... | |
| double [] | generatePath (double[] uniforms01) |
| Not implemented, requires two umontreal.ssj.rng.RandomStream ’s. | |
| double | nextObservation () |
| RandomStream | getStream () |
| Only returns a stream if both inner umontreal.ssj.rng.RandomStream ’s are the same. | |
| void | setStream (RandomStream stream, RandomStream otherStream) |
| Sets the streams. | |
| void | setStream (RandomStream stream) |
Sets both inner streams to stream. | |
| void | setOtherStream (RandomStream otherStream) |
Sets the otherStream, which is the stream used to choose between the two roots in the MSH method. | |
| RandomStream | getOtherStream () |
Returns the otherStream, which is the stream used to choose between the two quadratic roots from the MSH method. | |
| void | setNormalGen (NormalGen normalGen) |
| Sets the normal generator. More... | |
| NormalGen | getNormalGen () |
| Returns the normal generator. | |
 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 | |
| RandomStream | otherStream |
| NormalGen | normalGen |
| 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
Uses a faster generating method (MSH) [180] than the simple inversion of the distribution function used by InverseGaussianProcess.
It is about 60 times faster. However it requires two umontreal.ssj.rng.RandomStream ’s instead of only one for InverseGaussianProcess. The second stream is called otherStream below and it is used to randomly choose between two roots at each time step.
Constructor & Destructor Documentation
◆ InverseGaussianProcessMSH()
| InverseGaussianProcessMSH | ( | double | s0, |
| double | delta, | ||
| double | gamma, | ||
| RandomStream | stream, | ||
| RandomStream | otherStream | ||
| ) |
Constructs a new InverseGaussianProcessMSH.
The initial value s0 will be overridden by \(t[0]\) when the observation times are set.
Member Function Documentation
◆ generatePath() [1/2]
| double [] generatePath | ( | ) |
Generates the path.
It is done by successively calling nextObservation(), therefore the two umontreal.ssj.rng.RandomStream s are sampled alternatively.
◆ generatePath() [2/2]
| double [] generatePath | ( | double [] | unifNorm, |
| double [] | unifOther | ||
| ) |
Instead of using the internal streams to generate the path, uses two arrays of uniforms \(U[0,1)\).
The length of the arrays should be equal to the number of periods in the observation times. This method is useful for NormalInverseGaussianProcess.
◆ setNormalGen()
| void setNormalGen | ( | NormalGen | normalGen | ) |
Sets the normal generator.
It also sets one of the two inner streams to the stream of the normal generator.
The documentation for this class was generated from the following file:
- InverseGaussianProcessMSH.java
