Variational Bayes Inference for the DINA Model

Kazuhiro Yamaguchi and Kensuke Okada
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Kazuhiro Yamaguchi: 216185University of Iowa Japan Society for the Promotion of Science
Kensuke Okada: 13143University of Tokyo

Journal of Educational and Behavioral Statistics, 2020, vol. 45, issue 5, 569-597

Abstract: In this article, we propose a variational Bayes (VB) inference method for the deterministic input noisy AND gate model of cognitive diagnostic assessment. The proposed method, which applies the iterative algorithm for optimization, is derived based on the optimal variational posteriors of the model parameters. The proposed VB inference enables much faster computation than the existing Markov chain Monte Carlo (MCMC) method, while still offering the benefits of a full Bayesian framework. A simulation study revealed that the proposed VB estimation adequately recovered the parameter values. Moreover, an example using real data revealed that the proposed VB inference method provided similar estimates to MCMC estimation with much faster computation.

Keywords: variational inference; DINA model; cognitive diagnostic models (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:45:y:2020:i:5:p:569-597

DOI: 10.3102/1076998620911934

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