An Adaptive Importance Sampling Procedure.

Abstract

Monte Carlo calculations often require generation of a random sample of n-dimensional points drawn from a specified multivariate probability distribution. We present an importance sampling technique that can often greatly improve the efficiency of an acceptance/rejection generating method. The importance sampling function is defined as piecewise constant on a set of subregions, which are obtained by adaptively partitioning the sampling region so that the variation of density values within each subregion is relatively small. The partitioning strategy is based on multiparameter optimization to estimate the maximum and minimum of the original density function in each subregion. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1981
Accession Number
ADA110840

Entities

People

  • Jerome H. Friedman
  • Margaret H. Wright

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Contracts
  • Data Science
  • Demographic Cohorts
  • Efficiency
  • Information Science
  • Linear Accelerators
  • Military Research
  • Operations Research
  • Optimization
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Rejection
  • Sampling
  • Statistical Samples
  • United States

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Regression Analysis.