An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief
  Networks Usin
  Bayesian learning of belief networks (BLN) is a method for automatically
constructing belief networks (BNs) from data using search and Bayesian scoring
techniques. K2 is a particular instantiation of the method that implements a
greedy search strategy. To evaluate the accuracy of K2, we randomly generated a
number of BNs and for each of those we simulated data sets. K2 was then used to
induce the generating BNs from the simulated data. We examine the performance
of the program, and the factors that influence it. We also present a simple BN
model, developed from our results, which predicts the accuracy of K2, when
given various characteristics of the data set.
