Model Criticism of Bayesian Networks with Latent Variables

Autor:	Williamson, David M., Almond, Russell, Mislevy, Robert
Rok vydání:	2013
Předmět:	Computer Science - Artificial Intelligence Statistics - Applications Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1301.3902 Zobrazit plný text záznamu View this record from Arxiv