TABLE OF CONTENTS Recall that a Monte Carlo model is governed by a probability law that determines its random behavior. Except for very simple cases rarely encountered in practice, that law is too complicated to write down, and consequently, we cannot analytically derive system statistics either. Rather, we develop a simulation program that encapsulates that probability law by scheduling and processing random events. Each run of the simulation program, called a replication in simulation parlance, produces a sample system history from which various statistics are estimated via output analysis.
Output analysis is the modeling stage concerned with designing replications, computing statistics from them and presenting them in textual or graphical format. Thus, as its name suggests, output analysis focuses on the analysis of simulation results (output statistics). It provides the main value-added of the simulation enterprise by trying to understand system behavior and generate predictions for it. The main issues addressed by output analysis follow:
Replication design. A good design of simulation replications allows the analyst to obtain the most statistical information from simulation runs for the least computational cost. In particular, we seek to minimize the number of replications and their length, and still obtain reliable statistics.
Estimation of performance metrics. Replication statistics provide the data for computing point estimates and confidence intervals for system parameters of interest (see Section 3.10). Critical estimation issues are the size of the sample to be collected and the independence of observations used to compute statistics, particularly confidence intervals. Recall that estimates...
