The tutorial will be used to introduce some basic techniques for analysing the output of stochastic simulation models. Using examples, we will describe methods for determining the optimal warm-up length and number of replications as well as introducing ways of using simulation to compare different systems
We develop a class of techniques for analyzing the output of simulations of a semi-regenerative proc...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
The bootstrap is a simple but versatile technique for the statistical analysis of random simulations...
We present a brief overview of several of the basic output analysis techniques for evaluating stocha...
This is an introductory tutorial on the statistical analysis of simulation output, but focusing on t...
We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Ca...
A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also ...
We describe a method for comparing stochastic outputs of simulation models. The method is distributi...
This paper reviews statistical methods for analyzing output data from computer simulations. First, i...
This paper reviews statistical methods for analyzing output data from computer simulations of single...
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essential...
In this tutorial we consider the problem of finding the best set up to use for a system, where the o...
The steps of the process for conducting a simulation modeling and analysis project include: problem ...
Although most simulation practitioners are aware that output analysis is of extreme importance in a ...
In many studies of dynamic systems, the stochastic aspects are as important as the dynamic. It is th...
We develop a class of techniques for analyzing the output of simulations of a semi-regenerative proc...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
The bootstrap is a simple but versatile technique for the statistical analysis of random simulations...
We present a brief overview of several of the basic output analysis techniques for evaluating stocha...
This is an introductory tutorial on the statistical analysis of simulation output, but focusing on t...
We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Ca...
A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also ...
We describe a method for comparing stochastic outputs of simulation models. The method is distributi...
This paper reviews statistical methods for analyzing output data from computer simulations. First, i...
This paper reviews statistical methods for analyzing output data from computer simulations of single...
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essential...
In this tutorial we consider the problem of finding the best set up to use for a system, where the o...
The steps of the process for conducting a simulation modeling and analysis project include: problem ...
Although most simulation practitioners are aware that output analysis is of extreme importance in a ...
In many studies of dynamic systems, the stochastic aspects are as important as the dynamic. It is th...
We develop a class of techniques for analyzing the output of simulations of a semi-regenerative proc...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
The bootstrap is a simple but versatile technique for the statistical analysis of random simulations...