Accession Number:

ADA250602

Title:

Probabilistic Inference and Probabilistic Reasoning

Descriptive Note:

Corporate Author:

ROCHESTER UNIV NY DEPT OF PHILOSOPHY

Personal Author(s):

Report Date:

1989-01-01

Pagination or Media Count:

13.0

Abstract:

There are two profoundly different though not exclusive approaches to uncertain inference. According to one, uncertain inference leads from one distribution of non-extreme uncertainties among those propositions. According to the other, uncertain inference is like deductive inference in that the conclusion is detached from the premises the evidence and accepted as practically certain it differs in being non-monotonic and augmentation of the premises can lead to the withdrawal of conclusions already accepted. We show here, first, that probabilistic inference is what both traditional inductive logic ampliative inference and non-monotonic reasoning are designed to capture, third, that acceptance is legitimate and desirable, fourth, that statistical testing provides a model of probabilistic acceptance, and fifth, that a generalization of this model makes sense in AI.

Subject Categories:

  • Humanities and History
  • Psychology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE