Modeling Bayesian Networks by Learning from Experts
In: BNAIC 2005, 17-18 Oct 2005, Brussels, Belgium.
Bayesian network modeling by domain experts is still mainly a process
of trial and error. The structure of the graph and the speci¯cation of the
conditional probability tables (CPTs) are in practice often ¯ddled until a
desired model behavior is obtained. We describe a development tool in which
graph speci¯cation 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.