Importance Sampling for Stochastic Simulations.

Abstract

Importance Sampling is one of the classical variance reduction techniques for increasing the efficiency of Monte Carlo algorithms for estimating integrals. The basic idea is to replace the original random mechanism in the simulation by a new one and at the same time modify the function being integrated. In this paper the idea is extended to problems arising in the simulation of stochastic systems. Discrete-time Markov chains, continuous-time Markov chains, and generalized semi-Markov processes are covered. Applications are given to a GI/G/1 queueing problem and response surface estimation. Computation of the theoretical moments arising in importance sampling is discussed and some numerical examples given.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1987
Accession Number
ADA193585

Entities

People

  • Donald Iglehart
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computations
  • Contracts
  • Efficiency
  • Estimators
  • Integrals
  • Markov Chains
  • Markov Processes
  • Military Research
  • New York
  • Probability
  • Probability Distributions
  • Sampling
  • Simulations
  • Steady State
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.