A Theory of Bayesian Data Analysis

Abstract

Bayesian data analysis is concerned with the type of data manipulations, transformations, and just plain playing with the data, that any serious scientist engages in during the statistical (or other) analysis of his data. It is largely a post-data procedure, rather than a pre-data procedure, since even when it is desirable to think through such matters quite carefully prior to obtaining the data, in many real world experiments time and other constraints would provide limits on such activities. Compare Hacking or the discussion in Hodges concerning how much is enough. Bayesian data analysis goes beyond the mere data manipulations, however, and attempts to integrate the theory of subjective probability with such data analysis. In this respect it differs from other data-analytic approaches, which appear, more or less, to abandon probability. In this article the author attempts further to elucidate the theory of Bayesian data analysis begun in Hill. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Oct 10, 1989
Accession Number
ADA218420

Entities

People

  • Bruce M. Hill

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Biomedical
  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Data Analysis
  • Data Mining
  • Data Science
  • Decision Theory
  • Information Processing
  • Information Science
  • Judgment
  • Probability
  • Sequential Analysis
  • Statistical Algorithms
  • Statistical Decision Theory
  • Statistical Inference
  • Statistics
  • Theorems
  • Thinking

Readers

  • Business Analytics
  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference