  One of the most important aspects of current expert systems technology is the
ability to make causal inferences about the impact of new evidence. When the
domain knowledge and problem knowledge are uncertain and incomplete Bayesian
reasoning has proven to be an effective way of forming such inferences [3,4,8].
While several reasoning schemes have been developed based on Bayes Rule, there
has been very little work examining the comparative effectiveness of these
schemes in a real application. This paper describes a knowledge based system
for ship classification [1], originally developed using the PROSPECTOR updating
method [2], that has been reimplemented to use the inference procedure
developed by Pearl and Kim [4,5]. We discuss our reasons for making this
change, the implementation of the new inference engine, and the comparative
performance of the two versions of the system.
