Variational Bayes inference for hidden Markov diagnostic classification models
Copyright policy )Variational Bayes inference for hidden Markov diagnostic classification models
Abstract 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.
- University of Iowa United States
- University of Tsukuba Japan
Classification and discrimination; cluster analysis (statistical aspects), variational Bayes inference, Markov processes: estimation; hidden Markov models, cognitive diagnostic model, diagnostic classification model, longitudinal analysis, Bayes Theorem, Markov Chains, Markov chain Monte Carlo method, Humans, Computer Simulation, hidden Markov model, Monte Carlo Method, Algorithms, Applications of statistics to psychology
Classification and discrimination; cluster analysis (statistical aspects), variational Bayes inference, Markov processes: estimation; hidden Markov models, cognitive diagnostic model, diagnostic classification model, longitudinal analysis, Bayes Theorem, Markov Chains, Markov chain Monte Carlo method, Humans, Computer Simulation, hidden Markov model, Monte Carlo Method, Algorithms, Applications of statistics to psychology
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!