Sampling and Bayes' Inference in the Advancement of Learning

Abstract

Sampling theory inference (e.g. inference based on sampling distributions of statistics and in particular on significance tests) and Bayesian inference are usually thought of as rivals and much effort has been spent in propounding their relative merits. In this paper it is argued that both kinds of inference are needed in the scientific iteration whereby knowledge is acquired. This iteration employs a directed alternation between induction and deduction which uses model criticism on the one hand and parameter estimation on the other. An analysis of Bayes' formula reveals model criticism as a sampling theory concept and parameter estimation as a Bayesian concept. The implications of these ideas for robust estimation are discussed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1979
Accession Number
ADA077100

Entities

People

  • George E. Box

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Inference
  • Data Acquisition
  • Data Analysis
  • Data Mining
  • Data Science
  • Information Science
  • Iterations
  • Mathematics
  • Probability
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Samples
  • Statistics
  • Surveys
  • Theorems

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy