马尔科夫蒙特卡洛
吉布斯抽样
计算机科学
贝叶斯概率
蒙特卡罗方法
序数数据
统计
采样(信号处理)
罗伊特
计量经济学
人工智能
数学
机器学习
计算机视觉
滤波器(信号处理)
作者
A Paez Jimenez,James Balamuta,Steven Andrew Culpepper
摘要
Cognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic model for ordinal response data using a logit-link parameterization at the category level and extend the Pólya-gamma data augmentation strategy to ordinal response processes. A Gibbs sampling procedure is presented for efficient Markov chain Monte Carlo (MCMC) estimation methods. We provide results from a Monte Carlo study for model performance and present an application of the model.
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