Learning Bayesian Networks with Restricted Causal Interactions [article]

Julian R. Neil, Chris S. Wallace, Kevin B. Korb
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
more » ... n alternative interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric for structure learning of networks exhibiting causal independence, which we term first-order networks (FONs). We also investigate local model selection on a node-by-node basis.
arXiv:1301.6727v1 fatcat:bel2u3fiqjgzznyykx3nsrgntm