  There is an obvious need for improving the performance and accuracy of a
Bayesian network as new data is observed. Because of errors in model
construction and changes in the dynamics of the domains, we cannot afford to
ignore the information in new data. While sequential update of parameters for a
fixed structure can be accomplished using standard techniques, sequential
update of network structure is still an open problem. In this paper, we
investigate sequential update of Bayesian networks were both parameters and
structure are expected to change. We introduce a new approach that allows for
the flexible manipulation of the tradeoff between the quality of the learned
networks and the amount of information that is maintained about past
observations. We formally describe our approach including the necessary
modifications to the scoring functions for learning Bayesian networks, evaluate
its effectiveness through an empirical study, and extend it to the case of
missing data.

Artificial Intelligence;Machine Learning