Techniques for Efficient Monte Carlo Simulation. Volume 3. Variance Reduction

Abstract

Many Monte Carlo simulation problems lend themselves readily to the application of variance reduction techniques. These techniques can result in great improvements in simulation efficiency. The document describes the basic concepts of variance reduction (Part 1), and a methodology for application of variance reduction techniques is presented in Part 2. Appendices include the basic analytical expressions for application of variance reduction schemes as well as an abstracted bibliography. The techniques considered here include importance sampling, Russian roulette and splitting, systematic sampling, stratified sampling, expected values, statistical estimation, correlated sampling, history reanalysis, control variates, antithetic variates, regression, sequantial sampling, adjoint formulation, transformations, orthonormal and conditional Monte Carlo. Emphasis has been placed on presentation of the material for application by the general user. This has been accomplished by presenting a step by step procedure for selection and application of the appropriate technique(s) for a given problem.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1973
Accession Number
AD0762723

Entities

People

  • D. C. Irving
  • E. J. Mcgrath

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Boltzmann Equation
  • Computational Science
  • Data Science
  • Gamma Rays
  • Information Science
  • Knowledge Management
  • Materials
  • Monte Carlo Method
  • Operations Research
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Estimation
  • Statistical Sampling
  • Statistics
  • Stochastic Processes
  • Surveys

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.