Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems

Abstract

This paper discusses the application of the likelihood ratio gradient estimator to simulations of large Markovian models of highly dependable systems. Extensive empirical work, as well as some mathematical analysis of small dependability models, suggests that (in this model setting) the gradient estimators are not significantly more noisy than the estimates of the performance measures themselves. The paper also discusses implementation issues associated with likelihood ratio gradient estimation, as well as some theoretical complements associated with application of the technique to continuous-time Markov chains. Keywords: Highly dependable systems, Likelihood ratios, Importance sampling, Gradient estimators.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA227992

Entities

People

  • Ambuj Goyal
  • Marvin K. Nakayama
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Computer Simulations
  • Estimators
  • Failure Mode And Effect Analysis
  • Markov Chains
  • Mathematical Analysis
  • Mathematical Models
  • Military Research
  • Models
  • New York
  • Operations Research
  • Probability
  • Random Variables
  • Reliability
  • Sampling
  • Simulations
  • Steady State

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.