Represents a framework for performing experiments using simulation. More...
static int getRequiredNewObservations (StatProbe[] a, double targetError, double level) Returns the approximate number of additional observations required to reach a relative error smaller than or equal totargetError for each tally in the array a when confidence intervals are computed with confidence level level. More...
targetError for each tally enumerated by it when confidence intervals are computed with confidence level level. More...
probe. More...
ta. More...
fmmt. More...
center, computed using \(n=\) numberObs observations and with a confidence interval having radius \(\delta_n/\sqrt{n}=\) radius, to have a relative error less than or equal to \(\epsilon=\) targetError. More...
Represents a framework for performing experiments using simulation.
This class defines an abstract simulate method that should implement the simulation logic. It also provides utility methods to estimate the required number of additional observations that would be necessary for an estimator to reach a given precision, for sequential sampling.
This class is the base class of BatchMeansSim and RepSim implementing the logic for a simulation on infinite and finite horizon, respectively.
◆ SimExp()Constructs a new object performing experiments using the given simulator sim.
Returns the approximate number of additional observations required to reach a relative error smaller than or equal to targetError for each tally in the array a when confidence intervals are computed with confidence level level.
For each statistical collector in the given array, a confidence interval is computed independently of the other collectors, and an error check is performed by getRequiredNewObservations(StatProbe,double,double) to determine the required number of additional observations. The method returns the maximal number of required observations.
Returns the approximate number of additional observations required to reach a relative error smaller than or equal to targetError for each tally enumerated by it when confidence intervals are computed with confidence level level.
For each statistical collector returned by the iterator obtained from it, a confidence interval is computed independently of the other collectors, and an error check is performed by getRequiredNewObservations(StatProbe,double,double) to determine the required number of additional observations. The method returns the maximal number of required observations.
Calls getRequiredNewObservations(double,double,int,double) with the average, confidence interval radius, and number of observations given by the statistical probe probe.
This method always returns 0 if the probe is not a tally. For a umontreal.ssj.stat.Tally, the confidence interval is computed using umontreal.ssj.stat.Tally.confidenceIntervalStudent(double,double[]). For a umontreal.ssj.stat.FunctionOfMultipleMeansTally, it is computed using umontreal.ssj.stat.FunctionOfMultipleMeansTally.confidenceIntervalDelta(double,double[]).
Returns the approximate number of additional observations needed for the point estimator \(\bar{X}_n=\) center, computed using \(n=\) numberObs observations and with a confidence interval having radius \(\delta_n/\sqrt{n}=\) radius, to have a relative error less than or equal to \(\epsilon=\) targetError.
It is assumed that \(\bar{X}_n\) is an estimator of a mean \(\mu\), \(n\) is the number of observations numberObs, and that \(\delta_n/\sqrt{n}\to0\) when \(n\to\infty\).
If \(n\) is less than 1, this method returns 0. Otherwise, the relative error given by \(\delta_n/|\sqrt{n}\bar{X}_n|\) should be smaller than or equal to \(\epsilon\). If the inequality is true, this returns 0. Otherwise, the minimal \(n^*\) for which this inequality holds is approximated as follows. The target radius is given by \(\delta^*=\epsilon|\mu|\), which is approximated by \(\epsilon|\bar{X}_n|<\delta_n/\sqrt{n}\). The method must select \(n^*\) for which \(\delta_{n^*}/\sqrt{n^*}\le\delta^*\), which will be approximately true if \(\delta_{n^*}/\sqrt{n^*}\le\epsilon|\bar{X}_n|\). Therefore,
\[ n^*\ge(\delta_{n^*}/(\epsilon|\bar{X}_n|))^2\approx(\delta_n/(\epsilon|\bar{X}_n|))^2. \]
The method returns \(\mathrm{round}((\delta_n\sqrt{n}/(\epsilon|\bar{X}_n|))^2)-n\) where \(\mathrm{round}(\cdot)\) rounds its argument to the nearest integer.
radius or targetError are negative.
Calls getRequiredNewObservations(double,double,int,double) with the average, confidence interval radius, and number of observations given by the tally ta.
The confidence interval is computed using umontreal.ssj.stat.Tally.confidenceIntervalStudent(double,double[]).
Calls getRequiredNewObservations(double,double,int,double) with the average, confidence interval radius, and number of observations given by the function of multiple means fmmt.
The confidence interval is computed using umontreal.ssj.stat.FunctionOfMultipleMeansTally.confidenceIntervalDelta(double,double[]).
Determines if the simulation is in progress.
true if and only if simulation is in progress.
Sets the simulator associated with this experiment to sim.
This method should not be called while this object is simulating.
Performs an experiment whose logic depends on the used subclass.
Before starting the simulation, this method should set simulating to true, and reset it to false after the simulation is done. It is recommended to reset simulating to false inside a finally clause to prevent the indicator from remaining true in the case of error during simulation.
true when calling this method.
Returns the simulator linked to this experiment object.
The documentation for this class was generated from the following file:
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