Highly Influenced

# Bayesian Network Models for Local Dependence Among Observable Outcome Variables

@inproceedings{Almond2007BayesianNM, title={Bayesian Network Models for Local Dependence Among Observable Outcome Variables}, author={Russell G. Almond and Joris Mulder and Lisa A. Hemat and Duanli Yan and Brad Moulder and Frank Jenkins and Bruce Kaplan and Qingfeng Zhou and Youn-Hee Lim}, year={2007} }

- Published 2007

Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task that may be dependent. This paper explores four design patterns for modeling locally dependent observations from the same task: • No context—Ignore dependence among observables. • Compensatory context—Introduce a latent variable, context, to model task-specific knowledge and use a compensatory model to combine this with the relevant proficiencies… CONTINUE READING