Evaluating Bayesian Networks by Sampling with Simplified Assumptions
نویسندگان
چکیده
The most common fitness evaluation for Bayesian networks in the presence of data is the Cooper-Herskovitz criterion. This technique involves massive amounts of data and, therefore, expansive computations. We propose a cheaper alternative evaluation method using simplified assumptions which produces evaluations that are strongly correlated with the Cooper-Herskovitz criterion .
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