This book reviews the literature on graphical models from the statistics and artificial intelligence communities in which they are quite popular and looks at specific applications of these models to educational testing.

Features exercises to make the material concrete ECD portions of the book (Ch. 2, 12 & 13) build on work that was basis for the 2000 NCME award for Outstanding Technical Contribution to Educational Measurement received by the authors Includes basic review of Bayesian probability and statistics and an introduction to Evidence-Centered Design Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- An Introduction to Evidence-Centered Design.- Bayesian Probability and Statistics: a review.- Basic graph theory and graphical models.- Efficient calculations.- Some Example Networks.- Explanation and Test Construction.- Parameters for Bayesian Network Models.- Learning in Models with Fixed Structure.- Critiquing and Learning Model Structure.- An Illustrative Example.- The Conceptual Assessment Framework.- The Evidence Accumulation Process.- The Biomass Measurement Model.- The Future of Bayesian Networks in Educational Assessment.- Bayesian Network Resources.- References.