Model Criticism of Bayesian Networks with Latent Variables
This work addresses model criticism for Bayesian networks with latent variables in cognitive assessment and intelligent tutoring systems, representing an incremental advancement in this domain-specific area.
The paper tackles the challenge of empirically criticizing Bayesian networks with latent variables, particularly in cognitive assessment and tutoring systems, by introducing a methodology for global and local model criticism that identifies various misfit types, with results indicating potential for detecting and locating model errors.
The application of Bayesian networks (BNs) to cognitive assessment and intelligent tutoring systems poses new challenges for model construction. When cognitive task analyses suggest constructing a BN with several latent variables, empirical model criticism of the latent structure becomes both critical and complex. This paper introduces a methodology for criticizing models both globally (a BN in its entirety) and locally (observable nodes), and explores its value in identifying several kinds of misfit: node errors, edge errors, state errors, and prior probability errors in the latent structure. The results suggest the indices have potential for detecting model misfit and assisting in locating problematic components of the model.