Model criticism of Bayesian networks with latent variables

Authors:
David M. Williamson;Russell G. Almond;Robert J. Mislevy
Affiliations:
The Chauncey Group Intl., Princeton, NJ;Educational Testing Service, Princeton, NJ;Educational Testing Service, Princeton, NJ
Venue:
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Year:
2000

Citing 3
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Abstract

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.