Conditional dependence and decomposition strategies in diagnostic inference systems

Abstract This paper discusses the application of decomposition rules to Bayesian man—machine diagnostic inference systems. An experiment is reported which investigates the use of such decomposition rules in dealing with conditionally dependent data. The experiment, unlike its predecessors, employs an intuitive inference task where the veridical odds can be closely estimated. One major conclusion of this research is that logically equivalent decomposition rules are not necessarily of equal difficulty. Consequently, recommendations as to the superority of a particular decomposition rule should not be made; the systems designer's choice of a particular decision rule should be based on the types of data and conditional dependencies encountered in his particular situation. Such considerations may lead to the adoption of hybrid decomposition rules of a type not previously used.