Fundamental Problems in Applying Models and Predictive Mechanisms,

Abstract

Fundamental problems relating to representing reality in a model and inductive inference of conclusions from model results are presented and include correspondence of entities in theoretical and applied models, definition of fair sample, completeness, failure of nerve and imagination, and dependence of scientific belief and proper induction on scientific culture. A formal method for detecting potential variability of scientific belief is developed based on Bayes' Theorem. The appendix lists some rountine problems of model application. (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1971
Accession Number
AD0734166

Entities

People

  • Peter K. Luster

Tags

Readers

  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference