Accession Number : ADP005292

Title :   Uncertain Evidence and Artificial Analysis.

Descriptive Note : Research rept.,

Corporate Author : HARVARD UNIV CAMBRIDGE MASS DEPT OF STATISTICS

Personal Author(s) : Dempster,A. P. ; Kong,Augustine

Report Date : 16 MAR 1987

Pagination or Media Count : 17

Abstract : Belief function models form a natural basis for the construction of artificial analysts (eg, expert systems) capable of uncertain judgments. Random samples of various types may be used as the uncertain evidence which defines the necessary probability structure for the model. The paper develops some simple examples to illustrate these ideas. (Author)

Descriptors :   *INFORMATION THEORY, *MATHEMATICAL MODELS, ARTIFICIAL INTELLIGENCE, PROBABILITY, COMPUTATIONS, WORKSHOPS, STATISTICAL INFERENCE, BAYES THEOREM, MULTIVARIATE ANALYSIS

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE