Experimental Error Measurement in Monte Carlo Simulation

View Sample PDF

Author(s): Lucia Cassettari (University of Genoa, Italy), Roberto Mosca (University of Genoa, Italy)and Roberto Revetria (University of Genoa, Italy)
Copyright: 2010
Pages: 51
Source title: Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications
Source Author(s)/Editor(s): Evon M. O. Abu-Taieh (The Arab Academy for Banking and Financial Sciences, Jordan)and Asim A. El-Sheikh (Arab Academy for Banking and Financial Sector, Jordan)
DOI: 10.4018/978-1-60566-774-4.ch006

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

Purchase

View Experimental Error Measurement in Monte Carlo Simulation on the publisher's website for pricing and purchasing information.

Abstract

This chapter describes the set up step series, developed by the Genoa Research Group on Production System Simulation at the beginning of the ’80s, as a sequence, through which it is possible at first statistically validate the simulator, then estimate the variables which effectively affect the different target functions, then obtain, through the regression meta-models, the relations linking the independent variables to the dependent ones (target functions) and, finally, proceed to the detection of the optimal functioning conditions. The authors pay great attention to the treatment, the evaluation and control of the Experimental Error, under the form of Mean Square Pure Error (MSPE), a measurement which is always culpably neglected in the traditional experimentation on the simulation models but, that potentially can consistently invalidate with its magnitude the value of the results obtained from the model.

The IRMA Community

Research IRM

Experimental Error Measurement in Monte Carlo Simulation

Purchase

Abstract

Related Content

IRMA Sponsors