An Approach to Regenerative Simulation on a General State Space.

Abstract

A wide variety of stochastic systems may be viewed as Markov chains taking on values in a general state space. An example is the class of generalized semi-Markov processes, which are commonly obtained in network queueing problems via the technique of supplementary variables. A simulator is often interested in obtaining steady state properties of such a system. Some recent developments in Markov chain theory by Athreya, Ney, and Nummelin allow one to embed a certain subclass of these processes in a regenerative environment. We study some consequences of this embedding and develop statistical estimation procedures for the general problem that bear close resemblance to the regenerative method of simulation analysis for finite state Markov chains. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1980
Accession Number
ADA092221

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Convergence
  • Data Science
  • Embedding
  • Environment
  • Markov Chains
  • Markov Processes
  • Mathematical Models
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Simulators
  • Statistical Estimation
  • Statistics
  • Stochastic Processes
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design

Technology Areas

  • Space