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- W2969778518 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." @default.
- W2969778518 created "2019-08-29" @default.
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- W2969778518 date "2020-03-31" @default.
- W2969778518 modified "2023-10-16" @default.
- W2969778518 title "Variational Bayes Inference for the DINA Model" @default.
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- W2969778518 doi "https://doi.org/10.3102/1076998620911934" @default.
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