SSJ
3.3.1
Stochastic Simulation in Java

Tools for Simple Monte Carlo and QuasiMonte 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...  
Tools for Simple Monte Carlo and QuasiMonte Carlo Experiments.
This package offers elementary tools to help perform Monte Carlo and quasiMonte 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 realvalued performance, plus a vector of zeromean 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 quasiMonte 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 loglog scale.
The following examples show how to use this package. (To do... )