马尔科夫蒙特卡洛
贝叶斯定理
推论
计算
计算机科学
贝叶斯推理
近似贝叶斯计算
算法
统计推断
贝叶斯概率
蒙特卡罗方法
机器学习
数学优化
人工智能
数学
统计
作者
Kazuhiro Yamaguchi,Kensuke Okada
标识
DOI:10.3102/1076998620911934
摘要
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.
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