Likelihood Ratio Derivative Estimators for Stochastic Systems

Abstract

This paper discusses the likelihood ratio derivative estimation techniques for stochastic systems. After a brief review of the basic concepts, likelihood ratio derivative estimators are presented for the following classes of stochastic processes: time homogeneous discrete-time Markov chains, non-time homogeneous discrete-time Markov chains, time homogeneous continuous-time Markov chains, semi-Markov processes, non-time homogeneous continuous-time Markov chains, and generalized semi-Markov processes.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA213787

Entities

People

  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • California
  • Classification
  • Computer Simulations
  • Estimators
  • Markov Chains
  • Markov Processes
  • Monte Carlo Method
  • Operations Research
  • Probability
  • Procurement
  • Sampling
  • Security
  • Simulations
  • Statistical Algorithms
  • Stochastic Processes
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.