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Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models

Chen‐Wei Liu

British Journal of Mathematical and Statistical Psychology2026https://doi.org/10.1111/bmsp.70028article
ABDC B
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0.50

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.

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https://doi.org/https://doi.org/10.1111/bmsp.70028

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@article{chen‐wei2026,
  title        = {{Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models}},
  author       = {Chen‐Wei Liu},
  journal      = {British Journal of Mathematical and Statistical Psychology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/bmsp.70028},
}

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Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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