Theory Reenement on Bayesian Networks

Theory reenement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory reenement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incre-mental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally reened from data. Algorithms for reenement of Bayesian networks are presented to illustrate what is meant by \partial theory", \alternative theory repre-sentation", etc. The algorithms are an incre-mental variant of batch learning algorithms from the literature so can work well in batch and incremental mode.