Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-28T12:25:42.643Z Has data issue: false hasContentIssue false
  • English
  • Français

MAKING SIMULATIONS MORE EFFICIENT WHEN ANALYZING POISSON ARRIVAL SYSTEMS AND MEANS OF MONOTONE FUNCTIONS

Published online by Cambridge University Press:  06 March 2006

Sheldon M. Ross
Sheldon M. Ross
Affiliation:
Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA 90089-0193, smross@usc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

For a system in which arrivals occur according to a Poisson process, we give a new approach for using simulation to estimate the expected value of a random variable that is independent of the arrival process after some specified time t. We also give a new approach for using simulation to estimate the expected value of an increasing function of independent uniform random variables. Stratified sampling is a key technique in both cases.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 20 , Issue 2 , April 2006 , pp. 251 - 256
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Glasserman, P. (2004). Monte Carlo methods in financial engineering. New York: Springer.
Ross, S.M. (2002). Probability models for computer science. New York: Academic Press.
Ross, S.M. (2002). Simulation. New York: Academic Press.