Against Conditional Probability

Abstract

While a minority of statisticians hew to the Bayesian line, a large number of philosophers and a large number of AI researchers take Bayesian conditionalization for granted as the only way of updating uncertainties. At the same time, everybody, Bayesian or not, appears to accept the fundamental principle of direct inference: if you know the statistics, the statistics should constrain your belief. The contribution of this paper is to exhibit a conflict between these two principles, and to argue in favor of direct inference and against conditionalization.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA250344

Entities

People

  • Henry E. Kyburg Jr.

Organizations

  • University of Rochester

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Assembly Lines
  • Data Fusion
  • Data Science
  • Errors
  • Frequency
  • Information Processing
  • Information Science
  • Intervals
  • Mathematics
  • Measurement
  • Normal Distribution
  • Probability
  • Reasoning
  • Statistical Distributions
  • Statistics
  • Uncertainty

Readers

  • Artificial Intelligence
  • Government and Public Administration Law.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference