Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model
Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model
复制标题
饱和诊断分类模型的变分贝叶斯推理算法
DOI：
10.1007/s11336-020-09739-w
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发表时间：
2020
期刊：
影响因子：
3
通讯作者：
Okada Kensuke
中科院分区：
文献类型：
--
作者：
Yamaguchi Kazuhiro;Okada Kensuke
饱和诊断分类模型（DCM）可以灵活地适应属性之间的各种关系，以诊断个人的属性掌握，并包括各种重要的DCM作为子模型。然而，现有的配方饱和DCM是不更好地适合于推导有条件共轭先验的模型参数。由于它们的推导是发展变分贝叶斯（VB）推理算法的关键，在本研究中，我们提出了一种新的饱和DCM的混合配方。在此基础上，我们开发了一个VB推理算法的饱和DCM，使我们能够执行可扩展的和计算效率的贝叶斯估计。仿真研究表明，该算法可以在各种情况下恢复参数。它也已被证明，所提出的方法是特别适合的情况下，新的数据变得顺序随着时间的推移，如在计算机化的诊断测试。最后，通过对一个真实的教育数据集的分析，比较了VB算法和马尔可夫链蒙特卡罗（MCMC）算法的性能。结果表明，两种方法之间得到非常相似的估计，所提出的VB推理是远远快于MCMC。所提出的方法可以是一个实用的解决方案的计算负载的问题。
Saturated diagnostic classification models (DCM) can flexibly accommodate various relationships among attributes to diagnose individual attribute mastery, and include various important DCMs as sub-models. However, the existing formulations of the saturated DCM are not better suited for deriving conditionally conjugate priors of model parameters. Because their derivation is the key in developing a variational Bayes (VB) inference algorithm, in the present study, we proposed a novel mixture formulation of saturated DCM. Based on it, we developed a VB inference algorithm of the saturated DCM that enables us to perform scalable and computationally efficient Bayesian estimation. The simulation study indicated that the proposed algorithm could recover the parameters in various conditions. It has also been demonstrated that the proposed approach is particularly suited to the case when new data become sequentially available over time, such as in computerized diagnostic testing. In addition, a real educational dataset was comparatively analyzed with the proposed VB and Markov chain Monte Carlo (MCMC) algorithms. The result demonstrated that very similar estimates were obtained between the two methods and that the proposed VB inference was much faster than MCMC. The proposed method can be a practical solution to the problem of computational load.
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影响因子：
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作者：
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DOI：
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