Tools for Simple Monte Carlo and Quasi-Monte Carlo Experiments.
This package offers elementary tools to help perform Monte Carlo and quasi-Monte Carlo experiments with simple stochastic simulation models. To use these tools, the simulation model must be represented in a class that implements the MonteCarloModel interface. This interface requires in particular a method simulate that simulates the model and a method getPerformance to recover the performance measure(s) (or data) generated by the simulation. This performance can be any type of object. In the subclass MonteCarloModelDouble, it assumed to be a real number; this is the simplest case. In the class MonteCarloModelDoubleArray, the returned performance is assumed to be a double[] array, so a vector of several measures can be returned for each simulation run. In MonteCarloModelCV, it is assumed that the simulation produces a real-valued performance, plus a vector of zero-mean control variates.
The class MonteCarloExperiment provides methods to perform a simulation experiment that simulates the model for n independent runs and recover the data in statistical collectors, compute confidence intervals, make histograms, scatter plots, print a report, etc.
The class RQMCExperiment provides tools to perform similar experiments with randomized quasi-Monte Carlo (RQMC) instead. In an RQMC experiment, one simulates the system n times, using an RQMC point set of size n, and this is repeated m times by randomizing the point set independently for each of the m replications.
RQMCExperimentSeries offers tools to perform RQMC experiments for a series of several values of n, for example all powers of 2 in some interval, in order to study the behavior of RQMC estimators as a function of n. For example, one might be interested in estimating how the variance (and in some cases the bias) converges as a function of n, and perhaps plot it in log-log scale.
The following examples show how to use this package. (To do... )
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