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Using Hamiltonian Monte Carlo to estimate the log-linear cognitive diagnosis model via Stan
Zhehan Jiang1, Richard Carter2
1University of Alabama, 309E LB Gorgas Library, Tuscaloosa, AL, 35487, USA. zjiang17@ua.edu.
Abstract:
The Bayesian literature has shown that the Hamiltonian Monte Carlo (HMC) algorithm is powerful and efficient for statistical model estimation, especially for complicated models. Stan, a software program built upon HMC, has been introduced as a means of psychometric modeling estimation. However, there are no systemic guidelines for implementing Stan with the log-linear cognitive diagnosis model (LCDM), which is the saturated version of many cognitive diagnostic model (CDM) variants. This article bridges the gap between Stan application and Bayesian LCDM estimation: Both the modeling procedures and Stan code are demonstrated in detail, such that this strategy can be extended to other CDMs straightforwardly.
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