This class represents a gamma process [171] (page 82) \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\). More...
Public Member Functions | |
| GammaProcess (double s0, double mu, double nu, RandomStream stream) | |
Constructs a new GammaProcess with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\). More... | |
| GammaProcess (double s0, double mu, double nu, GammaGen Ggen) | |
Constructs a new GammaProcess with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\). More... | |
| double | nextObservation () |
| double | nextObservation (double nextT) |
Generates and returns the next observation at time \(t_{j+1} = \mathtt{nextTime}\), using the previous observation time \(t_j\) defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation \(X(t_j)\). More... | |
| double [] | generatePath () |
| Generates, returns and saves the path \(\{X(t_0), X(t_1), …, X(t_d)\}\). More... | |
| double [] | generatePath (double[] uniform01) |
| Generates, returns and saves the path \( \{X(t_0), X(t_1), …, X(t_d)\}\). More... | |
| void | setParams (double s0, double mu, double nu) |
| Sets the parameters \(S(t_0) = \mathtt{s0}\), \(\mu= \mathtt{mu}\) and \(\nu= \mathtt{nu}\) of the process. More... | |
| double | getMu () |
| Returns the value of the parameter \(\mu\). | |
| double | getNu () |
| Returns the value of the parameter \(\nu\). | |
| void | setStream (RandomStream stream) |
Resets the umontreal.ssj.rng.RandomStream of the umontreal.ssj.randvar.GammaGen to stream. | |
| RandomStream | getStream () |
Returns the umontreal.ssj.rng.RandomStream stream. | |
 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 | setLarger (double[] path, int left, int mid, int right) |
| double | setLarger (double[] path, int left, int right) |
| double | setLarger (double v) |
| void | init () |
| Protected Member Functions inherited from StochasticProcess | |
| void | init () |
Protected Attributes | |
| boolean | usesAnti = false |
| RandomStream | stream |
| GammaGen | Ggen |
| double | mu |
| double | muOverNu |
| double [] | mu2dtOverNu |
| 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 |
Static Protected Attributes | |
| static final double | EPS = 1.0e-15 |
Package Attributes | |
| double | nu |
| double | mu2OverNu |
Detailed Description
This class represents a gamma process [171] (page 82) \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\).
It is a continuous-time process with stationary, independent gamma increments such that for any \(\Delta t > 0\),
\[ S(t + \Delta t) = S(t) + X,\tag{GammaEqn} \]
where \(X\) is a random variate from the gamma distribution Gamma \((\mu^2\Delta t / \nu, \mu/ \nu)\).
In this class, the gamma process is sampled sequentially using equation ( GammaEqn ).
Constructor & Destructor Documentation
◆ GammaProcess() [1/2]
| GammaProcess | ( | double | s0, |
| double | mu, | ||
| double | nu, | ||
| RandomStream | stream | ||
| ) |
Constructs a new GammaProcess with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).
The gamma variates \(X\) in ( GammaEqn ) are generated by inversion using stream.
◆ GammaProcess() [2/2]
| GammaProcess | ( | double | s0, |
| double | mu, | ||
| double | nu, | ||
| GammaGen | Ggen | ||
| ) |
Constructs a new GammaProcess with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).
The gamma variates \(X\) in ( GammaEqn ) are supplied by the gamma random variate generator Ggen. Note that the parameters of the umontreal.ssj.randvar.GammaGen object Ggen are not important since the implementation forces the generator to use the correct parameters (as defined above).
Member Function Documentation
◆ generatePath() [1/2]
| double [] generatePath | ( | ) |
Generates, returns and saves the path \(\{X(t_0), X(t_1), …, X(t_d)\}\).
The gamma variates \(X\) in ( GammaEqn ) are generated using the umontreal.ssj.rng.RandomStream stream or the umontreal.ssj.rng.RandomStream included in the umontreal.ssj.randvar.GammaGen Ggen.
◆ generatePath() [2/2]
| double [] generatePath | ( | double [] | uniform01 | ) |
Generates, returns and saves the path \( \{X(t_0), X(t_1), …, X(t_d)\}\).
This method does not use the umontreal.ssj.rng.RandomStream stream nor the umontreal.ssj.randvar.GammaGen Ggen. It uses the vector of uniform random numbers \(U(0, 1)\) provided by the user and generates the path by inversion. The vector uniform01 must be of dimension \(d\).
◆ nextObservation()
| double nextObservation | ( | double | nextT | ) |
Generates and returns the next observation at time \(t_{j+1} = \mathtt{nextTime}\), using the previous observation time \(t_j\) defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation \(X(t_j)\).
Warning: This method will reset the observations time \(t_{j+1}\) for this process to nextT. The user must make sure that the \(t_{j+1}\) supplied is \(\geq t_j\).
◆ setParams()
| void setParams | ( | double | s0, |
| double | mu, | ||
| double | nu | ||
| ) |
Sets the parameters \(S(t_0) = \mathtt{s0}\), \(\mu= \mathtt{mu}\) and \(\nu= \mathtt{nu}\) of the process.
Warning: This method will recompute some quantities stored internally, which may be slow if called repeatedly.
The documentation for this class was generated from the following file:
- GammaProcess.java
