An empirical evaluation of antithetic variates and quasirandom points for simulating stochastic networks
- 1 January 1992
- journal article
- Published by SAGE Publications in SIMULATION
- Vol. 58 (1) , 23-31
- https://doi.org/10.1177/003754979205800105
Abstract
This paper is concerned with simulation procedures for estimating the distribution function of the time to complete stochastic activity networks. A conditional Monte Carlo method using uniformly directed cutsets is considered and combined with antithetic variate sampling and quasirandom points. The efficiency of the two variance reduction techniques is compared using CPU times and mean- square errors. By using three examples the study illustrates the decreasing benefits of using quasirandom points over antithetic variate technique as the network size increases.Keywords
This publication has 16 references indexed in Scilit:
- A Monte Carlo Technique with Quasirandom Points for the Stochastic Shortest Path ProblemAmerican Journal of Mathematical and Management Sciences, 1987
- Minimum Number Of Arcs In Conditional Monte Carlo Sampling Of Stochastic NetworksINFOR: Information Systems and Operational Research, 1986
- Estimating Network Characteristics in Stochastic Activity NetworksManagement Science, 1985
- Note—A Note on “Efficiency of the Antithetic Variate Method for Simulating Stochastic Networks”Management Science, 1983
- Quasi-Monte Carlo methods and pseudo-random numbersBulletin of the American Mathematical Society, 1978
- Conditional Monte Carlo: A Simulation Technique for Stochastic Network AnalysisManagement Science, 1971
- Simple stochastic networks: Some problems and proceduresNaval Research Logistics Quarterly, 1970
- Distribution of the Time Through a Directed, Acyclic NetworkOperations Research, 1965
- Algorithm 247: Radical-inverse quasi-random point sequenceCommunications of the ACM, 1964
- On large deviations of the empiric D. F. of vector chance variables and a law of the iterated logarithmPacific Journal of Mathematics, 1961