Sensitivity Analysis for Stationary Probabilities of a Markov Chain.

Abstract

This paper considers the problem of evaluating the sensitivity of a steady state cost alpha (Theta) to underlying uncertainty in a parameter vector 0 governing the probabilistic dynamics of the system under consideration. We show that the gradient grad alpha (Theta) plays a fundamental role in the parametric statistical theory for Markov processes. We then survey numerical methods available for evaluating grad alpha (Theta) and introduce a new Monte Carlo estimator for grad alpha (Theta), which is applicable to Markov processes of substantial generality. Keywords: simulation methodology; Markov chain; stationary distribution; Monte Carlo estimator; gradient estimation.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA178556

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Equations
  • Estimators
  • Integral Equations
  • Markov Chains
  • Markov Processes
  • Monte Carlo Method
  • Operations Research
  • Probability
  • Sensitivity
  • Statistical Algorithms
  • Statistical Inference
  • Steady State
  • Stochastic Processes
  • United States

Fields of Study

  • Mathematics

Readers

  • Fluid Dynamics.
  • Statistical inference.