This class represents a gamma process \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\), sampled using the gamma bridge method (see for example [208], [11] ). More...
Inheritance diagram for GammaProcessBridge:
Collaboration diagram for GammaProcessBridge:
Public Member Functions | |
| GammaProcessBridge (double s0, double mu, double nu, RandomStream stream) | |
Constructs a new GammaProcessBridge with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\). More... | |
| GammaProcessBridge (double s0, double mu, double nu, GammaGen Ggen, BetaGen Bgen) | |
Constructs a new GammaProcessBridge. More... | |
| double | nextObservation () |
| double | nextObservation (double nextT) |
| double [] | generatePath (double[] uniform01) |
| double [] | generatePath () |
| void | resetStartProcess () |
| void | setStream (RandomStream stream) |
Resets the umontreal.ssj.rng.RandomStream of the umontreal.ssj.randvar.GammaGen and the umontreal.ssj.randvar.BetaGen to stream. | |
 Public Member Functions inherited from GammaProcess | |
| 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 | init () |
| Protected Member Functions inherited from GammaProcess | |
| 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 | |
| BetaGen | Bgen |
| double | mu2OverNu |
| double [] | bMu2dtOverNuL |
| int [] | wIndexList |
| int | bridgeCounter = -1 |
| Protected Attributes inherited from GammaProcess | |
| 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 |
Package Attributes | |
| double | mu2dTOverNu |
| double [] | bMu2dtOverNuR |
| Package Attributes inherited from GammaProcess | |
| double | nu |
| double | mu2OverNu |
Additional Inherited Members | |
| Static Protected Attributes inherited from GammaProcess | |
| static final double | EPS = 1.0e-15 |
Detailed Description
This class represents a gamma process \(\{ S(t) = G(t; \mu, \nu) : t \geq0 \}\) with mean parameter \(\mu\) and variance parameter \(\nu\), sampled using the gamma bridge method (see for example [208], [11] ).
This is analogous to the bridge sampling used in BrownianMotionBridge.
Note that gamma bridge sampling requires not only gamma variates, but also beta variates. The latter generally take a longer time to generate than the former. The class GammaSymmetricalBridgeProcess provides a faster implementation when the number of observation times is a power of two.
The warning from class BrownianMotionBridge applies verbatim to this class.
Constructor & Destructor Documentation
◆ GammaProcessBridge() [1/2]
| GammaProcessBridge | ( | double | s0, |
| double | mu, | ||
| double | nu, | ||
| RandomStream | stream | ||
| ) |
Constructs a new GammaProcessBridge with parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).
Uses stream to generate the gamma and beta variates by inversion.
◆ GammaProcessBridge() [2/2]
| GammaProcessBridge | ( | double | s0, |
| double | mu, | ||
| double | nu, | ||
| GammaGen | Ggen, | ||
| BetaGen | Bgen | ||
| ) |
Constructs a new GammaProcessBridge.
Uses the random variate generators Ggen and Bgen to generate the gamma and beta variates, respectively. Note that both generator uses the same umontreal.ssj.rng.RandomStream. Furthermore, the parameters of the umontreal.ssj.randvar.GammaGen and umontreal.ssj.randvar.BetaGen objects are not important since the implementation forces the generators to use the correct parameters. (as defined in [208] (page 7)).
The documentation for this class was generated from the following file:
- GammaProcessBridge.java
