Related Experiment Video
Updated: Jan 22, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification
1Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan.
Abstract:
Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time-varying Q matrices, latent attributes, item parameters, attribute class proportions and transition matrices across multiple occasions. Using the R package hmdcm developed for this study, Monte Carlo simulations demonstrate accurate parameter recovery, and an empirical probability-concept assessment confirmed the algorithm's ability to trace attribute trajectories, supporting its value for longitudinal diagnostic classification in both research and instructional practice.
Related Concept Videos
Theory of Attribution I: Correspondent Inference Theory
Fundamental Attribution Error
Attribution Theory
Heart Failure IV: Classification and Diagnostic Evaluation
Attribution
Personal Choice and Fate Attributions

