Learning discrete Bayesian network parameters from continuous data streams: What is the best strategy?

We compare three approaches to learning numerical parameters of discrete Bayesian networks from continuous data streams: (1) the EM algorithm applied to all data, (2) the EM algorithm applied to data increments, and (3) the online EM algorithm. Our results show that learning from all data at each step, whenever feasible, leads to the highest parameter accuracy and model classification accuracy. When facing computational limitations, incremental learning approaches are a reasonable alternative. While the differences in speed between incremental algorithms are not large (online EM is slightly slower), for all but small data sets online EM tends to be more accurate than incremental EM.