
Accession Number : ADA180541
Title : Conditions Under Which A Markov Chain Converges to Its SteadyState in Finite Time.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CA DEPT OF OPERATIONS RESEARCH
Personal Author(s) : Glynn,Peter W. ; Iglehart,Donald L.
Report Date : APR 1987
Pagination or Media Count : 10
Abstract : Analysis of the initial transient problem of Monte Carlo steady state simulation motivates the following question for Markov chains: when does there exist a deterministic T such that P(x(T) = ybar(0) = x) = pi(y), where pi is the stationary distribution of X? We show that this can essentially never happen for a continuous time Markov chain; in discretetime, such processes are basically i.i.d. Keywords: Initial transient; Markov chains.
Descriptors : *CONVERGENCE, *MARKOV PROCESSES, TIME, TRANSIENTS, STEADY STATE
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE