BENEDICT: An Algorithm for Learning Probabilistic Belief Networks

We develop a system that, given a database containing instances of the variables in a domain of knowledge, captures many of the dependence relationships constrained by those data, and represents them as a belief network. To obtain the network structure, we have designed a new learning algorithm, called BENEDICT, which has been implemented and incorporated as a module within the system. The numerical component, i.e., the conditional probability tables, are estimated directly from the database. We have tested the system on databases generated from simulated networks by using probabil-istic sampling, including an extensive database , corresponding to the well-known Alarm Monitoring System. These databases were used as inputs for the learning module, and the networks obtained, compared with the originals, were consistently similar.