Uncertain Evidence and Artificial Analysis.
HARVARD UNIV CAMBRIDGE MASS DEPT OF STATISTICS
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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