Modeling Bayesian Networks by Learning from Experts
Bayesian network modeling by domain experts is still mainly a process of trial and error. The structure of the graph and the specification of the conditional probability tables (CPTs) are in practice often fiddled until a desired model behavior is obtained. We describe a development tool in which graph specification and CPT modeling are fully separated. Furthermore, the tuning of CPTs is handled automatically. The development tool consists of a database in which the graph description and the desired probabilistic behavior of the network are separately stored. From this database, the graph is constructed and the CPTs are numerically optimized in order to minimize the error between desired and actual behavior. The tool may be helpful in both development and maintenance of probabilistic expert systems. A demo is provided. A numerical example illustrates the methodology.