ERIC Number: EJ1266376
Record Type: Journal
Publication Date: 2020-Oct
Pages: 29
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1076-9986
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Variational Bayes Inference for the DINA Model
Journal of Educational and Behavioral Statistics, v45 n5 p569-597 Oct 2020
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.
Descriptors: Bayesian Statistics, Statistical Inference, Cognitive Measurement, Mathematics, Computation, Models
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
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Language: English
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