Epistemological Relevance and Statistical Knowledge

Abstract

For many years, at least since McCarthy and Hayes (1969), writers have lamented, and attempted to compensate for, the alleged fact that we often do not have adequate statistical knowledge for governing the uncertainty of belief, for making uncertain inferences, and the like. It is hardly ever spelled out what adequate statistical knowledge would be, if we had it, and how adequate statistical knowledge could be used to control and regulate epistemic uncertainty. Our purpose here is not to evaluate alternative treatments of uncertainty, but rather to explore the question of how far you can go on the basis of statistical knowledge that you do have, and what considerations must be taken account of in this attempt. Relatively few people have explored the question of how far you can go using statistical knowledge. Artificial Intelligence, Data Fusion Epistemological Relevance, Statistical Knowledge.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA250614

Entities

People

  • Henry E. Kyburg Jr.

Organizations

  • University of Rochester

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Classification
  • Data Fusion
  • Frequency
  • Handbooks
  • Language
  • Optical Scanning
  • Probability
  • Set Theory
  • Specific Volume
  • Standards
  • Statistics
  • Uncertainty
  • Universities

Readers

  • Artificial Intelligence
  • Theoretical Analysis.

Technology Areas

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