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 steady-state 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