Package umontreal.ssj.mcqmctools

Tools for Simple Monte Carlo and Quasi-Monte Carlo Experiments. More...

Packages
package	anova
	Tools to estimate ANOVA components for Monte Carlo models.

Classes
class	MonteCarloExperiment
	Provides generic tools to perform simple Monte Carlo experiments with a simulation model that implements one of the interfaces MonteCarloModelDouble, MonteCarloModelDoubleArray, or MonteCarloModelCV. More...
interface	MonteCarloModel
	An interface for a simple simulation model for which Monte Carlo (MC) or RQMC experiments are to be performed. More...
interface	MonteCarloModelCV
	An extension of MonteCarloModelDouble that also implements a vector of control variates. More...
interface	MonteCarloModelDensityKnown
	An interface for a simulation model for which Monte Carlo (MC) and RQMC experiments are to be performed. More...
interface	MonteCarloModelDouble
	An interface for a very simple simulation model for which Monte Carlo (MC) and RQMC experiments are to be performed. More...
interface	MonteCarloModelDoubleArray
	Similar to MonteCarloModelDouble except that the returned performance is an array of real numbers. More...
class	RQMCExperiment
	Provides basic generic tools to perform RQMC experiments with a simulation model that implements the MonteCarloModelDouble interface. More...
class	RQMCExperimentSeries
	This class offers facilities to perform experiments on the convergence of the variance when estimating a mean (expectation) with a series of RQMC point sets usually of the same type, but different sizes \(n\). More...

Detailed Description

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.

Examples

The following examples show how to use this package. (To do... )