Tal Boneh

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Most successful Bayesian network (BN) ap­ plications to date have been built through knowledge elicitation from experts. This is difficult and time consuming, which has lead to recent interest in automated methods for learning BNs from data. We present a case study in the construction of a BN in an intel­ ligent tutoring application, specifically dec­ imal(More)
A Bayesian Network ~BN! consists of a qualitative part representing the structural assumptions of the domain and a quantitative part, the parameters. To date, knowledge engineering support has focused on parameter elicitation, with little support for designing the graphical structure. Poor design choices in BN construction can impact the network's(More)
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