Sequential Gibbs Sampling Algorithm for Cognitive Diagnosis Models with Many Attributes
Bibliographic Data
| ID | 19290486 |
|---|---|
| Authors | Juntao Wang (0000-0002-1822-2176, Northeast Normal University), Ningzhong Shi (Northeast Normal University), Xue Zhang (0000-0001-5977-3639, Northeast Normal University, corresponding author), Gongjun Xu (0000-0003-4023-5413, University of Michigan, corresponding author) |
| Year | 2022 |
| Volume | 57 |
| Issue | 5 |
| Pages | 840-858 |
| Publication date | 2022-09-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2021.1896352 |
| PMID | 33755507 |
| OpenAlex | W3135994844 |
| Language | EN |
| Citations received | 1 |
| References cited | 33 |
Cognitive diagnosis models (CDMs) are useful statistical tools to provide rich information relevant for intervention and learning. As a popular approach to estimate and make inference of CDMs, the Markov chain Monte Carlo (MCMC) algorithm is widely used in practice. However, when the number of attributes, K, is large, the existing MCMC algorithm may become time-consuming, due to the fact that O(2K) calculations are usually needed in the process of MCMC sampling to get the conditional distribution for each attribute profile. To overcome this computational issue, motivated by Culpepper and Hudson's earlier work in 2018, we propose a computationally efficient sequential Gibbs sampling method, which needs O(K) calculations to sample each attribute profile. We use simulation and real data examples to show the good finite-sample performance of the proposed sequential Gibbs sampling, and its advantage over existing methods
Algorithm · Bayesian probability · Data mining · Gibbs sampling · Inference · Machine learning · Markov chain · Markov chain Monte Carlo · Monte Carlo method · Sample (material) · Sampling (signal processing) · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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Measurement of psychological disorders using cognitive diagnosis models.
Higher-Order Latent Trait Models for Cognitive Diagnosis
Inference from Iterative Simulation Using Multiple Sequences
The Multidimensional Testlet-Effect Cognitive Diagnostic Models
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |