Bayes Theorem from a Sampling-Resampling Perspective

Abstract

Even to the initiated, statistical calculations based on Bayes theorem can be daunting because of the numerical integrations required in all but the simplest applications. Moreover, from a teaching perspective, introductions to Bayesian statistics-if they are given at all-are circumscribed by these apparent calculational difficulties. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily implemented calculation strategies.

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Document Details

Document Type
Technical Report
Publication Date
Jul 31, 1991
Accession Number
ADA239515

Entities

People

  • A. E. Gelfand
  • A. F. Smith

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Inference
  • Data Science
  • Data Sets
  • Discrete Distribution
  • Information Science
  • Learning
  • Military Research
  • Numerical Integration
  • Probability
  • Rejection
  • Sampling
  • Statistics
  • Theorems
  • Two Dimensional
  • United States
  • United States Government

Readers

  • Calculus or Mathematical Analysis
  • Educational Psychology
  • Regression Analysis.

Technology Areas

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