Statistical Considerations in Computer Simulation Experiments.
Purchase
Purchase Print Copy
| Format | List Price | Price | |
|---|---|---|---|
| Add to Cart | Paperback7 pages | $20.00 | $16.00 20% Web Discount |
A tutorial given at a symposium on the interface between computer science and statistics sponsored by UCLA Extension, The American Statistical Association (Southern California Chapter), and the Association for Computing Machinery (Los Angeles Chapter), February 1, 1967. This paper describes some fundamental statistical problems that should be recognized at all stages of a system simulation that is, or contains, a queueing network, such as inventory models and job shop manufacturing facilities. Such a simulation is the generation of stochastic processes by Monte Carlo methods. While simulation languages such as SIMSCRIPT and GPSS have been developed and refined during the past decade, very little work has been done in applying statistical methodology to the analysis of computer simulations. (See also RM-4880, RM-5288.) 7 pp. Ref.
This report is part of the RAND Corporation Paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.
The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.