Learning Bayesian Networks with Restricted Causal Interactions
نویسندگان
چکیده
A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional prob ability 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 (Whit taker, 1990; Buntine, 1991; Neal, 1992; Heck erman and Meek, 1997), it is generally sub sumed under a naive Bayes model. We de scribe an alternative using a Minimum Mes sage Length (MML) (Wallace and Freeman, 1987) metric for the selection of local mod els with causal independence, which we term a first-order model (FOM). We also combine FOMs and full conditional models on a node by-node basis.
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