Variance Reduction Techniques for Simulating Markov Chains.
Abstract
Simulators frequently wish to estimate parameters of the limiting distribution of stable stochastic processes. Several new methods for reducing the variance of such estimates will be proposed and discussed. The methods are applicable to regenerative Markov processes in both discrete and continuous time as well as to semi-Markov processes. The methods are similar to the technique of multiple control variables yet differ in the important respect that it is not necessary to calculate the means of the controls. This is because the controls are chosen in such a way that their means actually equal the parameter of interest. The methods do require a certain amount of computation to be done before the simulation begins, although their cost should be relatively minor compared with that of the simulation. Numerical results demonstrating the effectiveness of the techniques for a simple queueing model are presented. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1977
- Accession Number
- ADA054274
Entities
People
- Philip Heidelberger
Organizations
- Stanford University