Chapter 10: A Hilbert Space Approach To Variance Reduction

Abstract

In this chapter we explain variance reduction techniques from the Hilbert space standpoint, in the terminating simulation context. We use projection ideas to explain how variance is reduced, and to link different variance reduction techniques. Our focus is on the methods of control variates, conditional Monte Carlo, weighted Monte Carlo, stratification, and Latin hypercube sampling.

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Document Details

Document Type
Technical Report
Publication Date
Nov 16, 2005
Accession Number
ADA495125

Entities

People

  • Roberto Szechtman

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Equations
  • Estimators
  • Functional Analysis
  • Hilbert Space
  • Mathematics
  • Monte Carlo Method
  • New York
  • Numerical Analysis
  • Operations Research
  • Probability
  • Random Variables
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Stratification

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Linear Algebra
  • Regression Analysis.

Technology Areas

  • Space