Variational Bayes inference for hidden Markov diagnostic classification models

Diagnostic classification models (DCMs) can be used to track the cognitive learning states of students across multiple time points or over repeated measurements. This study developed an effective variational Bayes (VB) inference method for hidden Markov longitudinal general DCMs. The simulations per...

Full description

Saved in:
Bibliographic Details
Published in:British journal of mathematical & statistical psychology 2024-02, Vol.77 (1), p.55-79
Main Authors: Yamaguchi, Kazuhiro, Martinez, Alfonso J.
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Classification
cognitive diagnostic model
Computer Simulation
diagnostic classification model
Diagnostic systems
hidden Markov model
Humans
Inference
longitudinal analysis
Markov chain Monte Carlo method
Markov Chains
Mathematical models
Monte Carlo Method
Parameter estimation
variational Bayes inference
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diagnostic classification models (DCMs) can be used to track the cognitive learning states of students across multiple time points or over repeated measurements. This study developed an effective variational Bayes (VB) inference method for hidden Markov longitudinal general DCMs. The simulations performed in this study verified the validity of the proposed algorithm for satisfactorily recovering true parameters. Simulation and applied data analyses were conducted to compare the proposed VB method to Markov chain Monte Carlo (MCMC) sampling. The results revealed that the parameter estimates provided by the VB method were consistent with the MCMC method with the additional benefit of a faster estimation time. The comparative simulation also indicated differences between the two methods in terms of posterior standard deviation and coverage of 95% credible intervals. Thus, with limited computational resources and time, the proposed VB method can output estimations comparable to that of MCMC.
ISSN:0007-1102
2044-8317
DOI:10.1111/bmsp.12308