Monte Carlo Approximations in Bayesian Decision Theory. Part 3. Limiting Behavior of Monte Carlo Approximations

Abstract

Monte Carlo approximation is a useful method in obtaining a numerical approximation to a Bayesian action (an action which minimizes the posterior expected loss). We study the behavior of the Monte Carlo approximation when the Monte Carlo sample size is large. Convergence and convergence rate of the Monte Carlo approximation are established under some weak conditions on the loss function.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA204173

Entities

People

  • Jun Shao

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Bayesian Networks
  • Contracts
  • Convergence
  • Data Science
  • Decision Theory
  • Information Science
  • Military Research
  • Monte Carlo Method
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • Universities

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms