
Accession Number : ADA115451
Title : Simulating a Markov Chain with a Superefficient Sampling Method.
Descriptive Note : Technical rept.,
Corporate Author : NORTH CAROLINA UNIV AT CHAPEL HILL CURRICULUM IN OPERATIONS RESEARCH AND SYSTEMS ANALYSIS
Personal Author(s) : Fishman,George S
PDF Url : ADA115451
Report Date : Apr 1982
Pagination or Media Count : 42
Abstract : This paper describes an algorithm and a FORTRAN subprogram, CHAIN, for simulating the behavior of an (n+1) state Markov chain using a variance reducing technique called rotation sampling. The simulation of k microreplications is carried out in parallel at a mean cost or = O(1n k) and with variances of sample quantities of interest or = O((1n k squared)/k squared). The program allows for independent macroreplications, each of k microreplications, in order to faciliate estimation of the variances of sample quantities of interest. The paper describes theoretical results that underlie the algorithm and program in Section 1 and presents applications of interest for first passage time and steadystate distributions in Section 2. Section 3 describes the algorithm and CHAIN and an example in Section 4 illustrates how CHAIN works in practice. Section 5 describes the options available for restarting the simulation. (Author)
Descriptors : *Markov processes, *Simulation, *Sampling, *Methodology, Efficiency, Costs, Mean, Rotation, Distribution, Steady state, Estimates, Analysis of variance
Subject Categories : Statistics and Probability
Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE