CMU Artificial Intelligence Repository
Home INFO Search FAQs Repository Root

RCSTAT: Produces probability intervals from raw data using the Kyburg/Pollock method.

Few uncertainty reasoning programs go directly from raw observations to probabilities. Statistical techniques in the Neyman-Pearson tradition are widely available, but require more expert intervention than is acceptable for some AI applications. The main problem is identifying the reference class: as past instances are required to resemble the target more closely, the sample of such instances diminishes. The logic for finding the right balance between relevance and sufficient sample size is not treated in mathematical statistics, but is treated in the philosophical foundations of probability.

   Email from R. Loui.

Version: 9-DEC-92 Requires: C; compile with math library (cc rcstat.c -lm) Copying: Copyright (c) 1992 by A. Costello, R. Loui Use, copying, and distribution permitted, provided that RCSTAT (and derivatives) are not included in a commercial product. CD-ROM: Prime Time Freeware for AI, Issue 1-1 Author(s): A. Costello Contact: Dr. R. P. Loui Dept. of Computer Science Washington University St. Louis, MO 63130 Tel: 314-935-6102 Keywords: Authors!Costello, Authors!Loui, C!Code, Data, Defeasible Reasoning, Induction, Probabilistic Reasoning, RCSTAT, Reasoning!Defeasible Reasoning, Reasoning!Probabilistic Reasoning, Reasoning!Statistical Reasoning, Reference Class, Statistical Reasoning, Statistics, Uncertainty References: A user's manual and typescript may be found at the top of the source code. Other relevant references include: H. Kyburg, PROBABILITY AND THE LOGIC OF RATIONAL BELIEF, Wesleyan, 1961. H. Kyburg, LOGICAL FOUNDATIONS OF STATISTICAL INFERENCE, Reidel, 1974. H. Kyburg, ``The reference class,'' PHILOSOPHY OF SCIENCE 50, 1982. H. Kyburg, EPISTEMOLOGY AND INFERENCE,Minnesota, 1983. J. Pollock, ``A theory of direct inference,'' THEORY AND DECISION 16, 1983. J. Pollock, ``Foundations for direct inference,'' THEORY AND DECISION 17, 1984. J. Pollock, NOMIC PROBABILITY AND THE FOUNDATIONS OF INDUCTION, Oxford, 1990. R. Loui, ``Computing reference classes,'' UNCERTAINTY IN AI 2, 1987.
Last Web update on Mon Feb 13 10:27:52 1995