public double nextObservation() {
double x = path[observationIndex];
x += mudt[observationIndex] + sigmasqrdt[observationIndex] * gen.nextDouble();
observationIndex++;
path[observationIndex] = x;
return x;
}
public double[] generatePath() {
double x = x0;
for (int j = 0; j < d; j++) {
x += mudt[j] + sigmasqrdt[j] * gen.nextDouble();
path[j + 1] = x;
}
observationIndex = d;
observationCounter = d;
return path;
}
/**
* Generates and returns the next observation at time @f$t_{j+1} =@f$ `nextTime`. It uses the
* previous observation time @f$t_j@f$ defined earlier (either by this method or by
* <tt>setObservationTimes</tt>), as well as the value of the previous observation @f$X(t_j)@f$.
* *Warning*: This method will reset the observations time @f$t_{j+1}@f$ for this process to
* `nextTime`. The user must make sure that the @f$t_{j+1}@f$ supplied is @f$\geq t_j@f$.
*/
public double nextObservation(double nextTime) {
// This method is useful for generating variance gamma processes
double x = path[observationIndex];
double previousTime = t[observationIndex];
observationIndex++;
t[observationIndex] = nextTime;
double dt = nextTime - previousTime;
x += mu * dt + sigma * Math.sqrt(dt) * gen.nextDouble();
path[observationIndex] = x;
return x;
}
/**
* Generates an observation of the process in `dt` time units, assuming that the process has
* value @f$x@f$ at the current time. Uses the process parameters specified in the constructor.
* Note that this method does not affect the sample path of the process stored internally (if
* any).
*/
public double nextObservation(double x, double dt) {
x += mu * dt + sigma * Math.sqrt(dt) * gen.nextDouble();
return x;
}