Regenerative Aspects of the Steady-State Simulation Problem for Markov Chains.

Abstract

The general discrete-event simulation can be viewed, by using the technique of supplementary variables, as a Markov chain living in a general state space. For such chains, we can define in precise terms, the notion of an associated well-posed steady-state simulation problem. We prove that the concept of well-posedness is equivalent to assuming that the Markov chain has regenerate-type structure. These two conditions are, in turn, equivalent to assuming a certain smoothness on the transition probabilities of the chain. We also consider two examples which illustrate how a chain can fail to have regenerate-type structure. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1982
Accession Number
ADA119232

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Convergence
  • Embedding
  • Environment
  • Intervals
  • Markov Chains
  • Markov Processes
  • Military Research
  • Operations Research
  • Probability
  • Random Variables
  • Sequences
  • Simulations
  • Simulators
  • Steady State
  • Stochastic Processes
  • Theorems
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Statistical inference.

Technology Areas

  • Space