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Learning Bayesian Networks with Restricted Causal Interactions
[article]
2013
arXiv
pre-print
A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991; Neal, 1992; Heckerman and Meek, 1997), for structure learning they are generally subsumed under a naive Bayes model. We describe
arXiv:1301.6727v1
fatcat:bel2u3fiqjgzznyykx3nsrgntm