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|  | GammaProcessPCABridge (double s0, double mu, double nu, RandomStream stream) | 
|  | Constructs a new GammaProcessPCABridgewith parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).  More...
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| double [] | generatePath (double[] uniform01) | 
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| double [] | generatePath () | 
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| void | setParams (double s0, double mu, double nu) | 
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| void | setObservationTimes (double[] t, int d) | 
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| BrownianMotionPCA | getBMPCA () | 
|  | Returns the inner BrownianMotionPCA. 
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|  | GammaProcessPCA (double s0, double mu, double nu, RandomStream stream) | 
|  | Constructs a new GammaProcessPCAwith parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).  More...
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|  | GammaProcessPCA (double s0, double mu, double nu, GammaGen Ggen) | 
|  | Constructs a new GammaProcessPCAwith parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).  More...
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| double [] | generatePath () | 
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| double [] | generatePath (double[] uniform01) | 
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| double | nextObservation () | 
|  | This method is not implemented in this class since the path cannot be generated sequentially. 
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| double | nextObservation (double nextT) | 
|  | This method is not implemented in this class since the path cannot be generated sequentially. 
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| BrownianMotionPCA | getBMPCA () | 
|  | Returns the BrownianMotionPCA that is included in the GammaProcessPCA object. 
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| void | setObservationTimes (double[] t, int d) | 
|  | Sets the observation times of the GammaProcessPCA and the BrownianMotionPCA. 
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| void | setParams (double s0, double mu, double nu) | 
|  | Sets the parameters s0, \(\mu\) and \(\nu\) to new values, and sets the variance parameters of the BrownianMotionPCA to \(\nu\).
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| void | setStream (RandomStream stream) | 
|  | Resets the umontreal.ssj.rng.RandomStream of the gamma generator and the umontreal.ssj.rng.RandomStream of the inner BrownianMotionPCA to stream.
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|  | GammaProcess (double s0, double mu, double nu, RandomStream stream) | 
|  | Constructs a new GammaProcesswith parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).  More...
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|  | GammaProcess (double s0, double mu, double nu, GammaGen Ggen) | 
|  | Constructs a new GammaProcesswith parameters \(\mu= \mathtt{mu}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).  More...
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| double | nextObservation () | 
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| 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...
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| double [] | generatePath () | 
|  | Generates, returns and saves the path \(\{X(t_0), X(t_1), …, X(t_d)\}\).  More... 
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| double [] | generatePath (double[] uniform01) | 
|  | Generates, returns and saves the path \( \{X(t_0), X(t_1), …, X(t_d)\}\).  More... 
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| 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... 
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| double | getMu () | 
|  | Returns the value of the parameter \(\mu\). 
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| double | getNu () | 
|  | Returns the value of the parameter \(\nu\). 
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| void | setStream (RandomStream stream) | 
|  | Resets the umontreal.ssj.rng.RandomStream of the umontreal.ssj.randvar.GammaGen to stream.
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| RandomStream | getStream () | 
|  | Returns the umontreal.ssj.rng.RandomStream stream.
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| void | setObservationTimes (double[] T, int d) | 
|  | Sets the observation times of the process to a copy of T, with.  More...
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| void | setObservationTimes (double delta, int d) | 
|  | Sets equidistant observation times at \(t_j = j\delta\), for.  More... 
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| double [] | getObservationTimes () | 
|  | Returns a reference to the array that contains the observation times.  More... 
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| int | getNumObservationTimes () | 
|  | Returns the number \(d\) of observation times, excluding the time \(t_0\). 
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| abstract double [] | generatePath () | 
|  | Generates, returns, and saves the sample path \(\{X(t_0), X(t_1), \dots, X(t_d)\}\).  More... 
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| double [] | generatePath (RandomStream stream) | 
|  | Same as generatePath(), but first resets the stream tostream.
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| double [] | getPath () | 
|  | Returns a reference to the last generated sample path \(\{X(t_0), ... , X(t_d)\}\).  More... 
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| void | getSubpath (double[] subpath, int[] pathIndices) | 
|  | Returns in subpaththe values of the process at a subset of the observation times, specified as the times \(t_j\) whose indices.  More...
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| double | getObservation (int j) | 
|  | Returns \(X(t_j)\) from the current sample path.  More... 
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| 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... 
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| boolean | hasNextObservation () | 
|  | Returns trueif \(j<d\), where \(j\) is the number of observations of the current sample path generated since the last call to resetStartProcess.  More...
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| double | nextObservation () | 
|  | Generates and returns the next observation \(X(t_j)\) of the stochastic process.  More... 
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| int | getCurrentObservationIndex () | 
|  | Returns the value of the index \(j\) corresponding to the time.  More... 
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| double | getCurrentObservation () | 
|  | Returns the value of the last generated observation \(X(t_j)\). 
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| double | getX0 () | 
|  | Returns the initial value \(X(t_0)\) for this process. 
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| void | setX0 (double s0) | 
|  | Sets the initial value \(X(t_0)\) for this process to s0, and reinitializes.
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| abstract void | setStream (RandomStream stream) | 
|  | Resets the random stream of the underlying generator to stream.
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| abstract RandomStream | getStream () | 
|  | Returns the random stream of the underlying generator. 
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| 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... 
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Same as GammaProcessPCA, but the generated uniforms correspond to a bridge transformation of the BrownianMotionPCA instead of a sequential transformation.