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Bayesian networks (BNs) have become increasingly popular tools for learning and prediction in the context of user modeling. However, due to the lacks of individual training data especially for inactive or new users, separate treatments of individual users often become quite problematic. In this...
Many studies on learning Bayesian networks have used the Dirichlet prior score metric (DPSM). Although they assume different optimum hyper-parameter values for DPSM, few studies have focused on selection of optimum hyper-parameter values. Analyses of DPSM hyper-parameters for learning Bayesian...
This paper proposes a collaborative filtering method for massive datasets that is based on Bayesian networks. We first compare the prediction accuracy of four scoring-based learning Bayesian networks algorithms (AIC, MDL, UPSM, and BDeu) and two conditional-independence-based (Cl-based) learning...
This paper describes a Bayesian network model for a candidate assessment design that had four proficiency variables and 48 tasks with 3–12 observable outcome variables per task and scale anchors to identify the location of the subscales. The domain experts’ view of the relationship among...
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