An Improved Strategy for Bayesian Estimation of the Reduced Reparameterized Unified Model
An Improved Strategy for Bayesian Estimation of the Reduced Reparameterized Unified Model
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DOI：
10.1177/0146621617707511
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发表时间：
2018-03-01
影响因子：
1.2
通讯作者：
Hudson, Aaron
中科院分区：
文献类型：
--
作者：
Culpepper, Steven Andrew;Hudson, Aaron
一个流行的合取认知诊断模型，减少重新参数化统一模型（rRUM）的贝叶斯公式，开发。新的贝叶斯公式的rRUM采用了潜在的响应数据增强策略，产生易处理的全条件分布。描述了一种Gibbs抽样算法来近似rRUM参数的后验分布。蒙特卡洛研究支持准确的参数恢复，并提供证据表明，吉布斯采样器往往收敛在更少的迭代，并有一个更大的有效样本量比常用的大都会黑斯廷斯算法。所开发的方法作为R包名为rRUM传播给应用研究人员。
A Bayesian formulation for a popular conjunctive cognitive diagnosis model, the reduced reparameterized unified model (rRUM), is developed. The new Bayesian formulation of the rRUM employs a latent response data augmentation strategy that yields tractable full conditional distributions. A Gibbs sampling algorithm is described to approximate the posterior distribution of the rRUM parameters. A Monte Carlo study supports accurate parameter recovery and provides evidence that the Gibbs sampler tended to converge in fewer iterations and had a larger effective sample size than a commonly employed Metropolis-Hastings algorithm. The developed method is disseminated for applied researchers as an R package titled rRUM.