ACM Portal
  Subscribe (Full Service)    Register (Limited Service, Free)    Login
  Search:   The ACM Digital Library   The Guide
  
ACM Home Page
Please provide us with feedback. Feedback
Output interpretation: some myths and common errors in simulation experiments
Full text PdfPdf (123 KB)
Source Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents
Arlington, Virginia
TUTORIAL SESSION: Introductory tutorials table of contents
Pages: 39 - 46  
Year of Publication: 2001
ISBN:0-7803-7309-X
Author
Bruce W. Schmeiser  Purdue University, West Lafayette, IN
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 16,   Citation Count: 7
Additional Information:

abstract   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

During the more than fifty years that Monte Carlo simulation experiments have been performed on digital computers, a wide variety of myths and common errors have evolved. We discuss some of them, with a focus on probabilistic and statistical issues.


CITED BY  7
Russell R. Barton , Stephen E. Chick , Russell C. H. Cheng , Shane G. Henderson , Averill M. Law , Bruce W. Schmeiser , Lawrence M. Leemis , Lee W. Schruben , James R. Wilson, Panel discussion on current issues in input modeling: panel on current issues in simulation input modeling, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
Bruce Schmeiser , Yingchieh Yeh, Output analysis: on choosing a single criterion for confidence-interval procedures, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
Lawrence Leemis, Input modeling: input modeling, Proceedings of the 35th conference on Winter simulation: driving innovation, December 07-10, 2003, New Orleans, Louisiana
John S. Carson, II, Verification validation: model verification and validation, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
Stewart Robinson, Conceptual modeling for simulation: issues and research requirements, Proceedings of the 37th conference on Winter simulation, December 03-06, 2006, Monterey, California
Bruce Schmeiser, Simulation output analysis: a tutorial based on one research thread, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
Lawrence M. Leemis, Building credible input models, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.8 Types of Simulation
          Subjects: Monte Carlo

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.6 Simulation Output Analysis


General Terms:
Algorithms, Design, Performance

Collaborative Colleagues:
Bruce W. Schmeiser: colleagues

The ACM Portal is published by the Association for Computing Machinery. Copyright © 2009 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us

Useful downloads: Adobe Acrobat    QuickTime    Windows Media Player    Real Player