Model Criticism of Bayesian Networks with Latent Variables
David Williamson, Russell Almond, Robert Mislevy
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.
Keywords: Bayesian Networks, model criticism, latent variables, model fit, cognitive, assessmen
PDF Link: /papers/00/p634-williamson.pdf
AUTHOR = "David Williamson
and Russell Almond and Robert Mislevy",
TITLE = "Model Criticism of Bayesian Networks with Latent Variables",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "634--643"