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

Agosta, 2008, Query-based diagnostics, 1 Cappe, 2011, Online expectation maximisation, 31 Carbonara, 1998, A comparison of batch and incremental supervised learning algorithms, 264 Cooper, 1990, The computational complexity of probabilistic inference using Bayesian belief networks, Artif. Intell., 42, 393, 10.1016/0004-3702(90)90060-D Cooper, 1992, A Bayesian method for the induction of probabilistic networks from data, Mach. Learn., 9, 309, 10.1007/BF00994110 Dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc., Ser. B, 39, 1 Frank Gama, 2010 He, 2011, Incremental learning from stream data, IEEE Trans. Neural Netw., 22, 1901, 10.1109/TNN.2011.2171713 Holmes, 2004 Kokolakis, 2001, Bayesian multivariate micro-aggregation under the Hellinger's distance criterion, Res. Off. Stat., 4, 117 Kullback, 1951, On information and sufficiency, Ann. Math. Stat., 22, 79, 10.1214/aoms/1177729694 Lauritzen, 1995, The EM algorithm for graphical association models with missing data, Comput. Stat. Data Anal., 19, 191, 10.1016/0167-9473(93)E0056-A Liang, 2009, Online EM for unsupervised models, 611 Łupińska-Dubicka, 2012, A comparison of popular fertility awareness methods to a DBN model of the woman's monthly cycle, 219 Olesen, 1992, aHUGIN: a system creating adaptive causal probabilistic networks, 223 Oniśko, 2013, Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems, Artif. Intell. Med., 57, 197, 10.1016/j.artmed.2013.01.004 Pearl, 1988 Sato, 2000, On-line EM algorithm for the normalized Gaussian network, Neural Comput., 12, 407, 10.1162/089976600300015853 Wilson, 2003, The general inefficiency of batch training for gradient descent learning, Neural Netw., 16, 1429, 10.1016/S0893-6080(03)00138-2