Conditions Under Which A Markov Chain Converges to Its Steady-State in Finite Time.

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) = y-bar-(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 discrete-time, such processes are basically i.i.d. Keywords: Initial transient; Markov chains.

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1987
Accession Number
ADA180541

Entities

People

  • Donald Iglehart
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Markov Chains
  • Markov Processes
  • Simulations
  • Stationary
  • Steady State

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.