Sequential Update of Bayesian Network Structure
N. Friedman and M. Goldszmidt
To appear in Proc. Thirteenth Conf. on
Uncertainty in Artificial Intelligence (UAI 97).
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 and
empirical study, and extend it to the case of missing data.
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