  This paper examines the accuracy of the PROSPECTOR model for uncertain
reasoning. PROSPECTOR's solutions for a large number of computer-generated
inference networks were compared to those obtained from probability theory and
minimum cross-entropy calculations. PROSPECTOR's answers were generally
accurate for a restricted subset of problems that are consistent with its
assumptions. However, even within this subset, we identified conditions under
which PROSPECTOR's performance deteriorates.
