A Gibbs Sampling Algorithm with Monotonicity Constraints for Diagnostic Classification Models

Abstract excerpt

<p>Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-class equality constraints and additional monotonicity constraints on their item parameters, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicit...

Topics

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• Algorithms
• Bayes Theorem
• Cluster Analysis
• Clustering Algorithms
• Hearing Loss, Sensorineural
• Phenotype
• Reproducibility of Results
• Unsupervised Machine Learning

Identifiers and source

Literature Corpus work

defe9d57-460e-5eae-89b2-02d8b7954b58

DOI

10.31234/osf.io/undcv