Learning large \(Q\)-matrix by restricted Boltzmann machines

DOI10.1007/s11336-021-09828-4zbMath1496.62208arXiv2006.15424OpenAlexW4210696749MaRDI QIDQ2088924

Gongjun Xu, Chengcheng Li, Chenchen Ma

Publication date: 6 October 2022

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.15424

zbMATH Keywords

restricted Boltzmann machines\(Q\)-matrixcognitive diagnosis models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Applications of statistics to psychology (62P15)

Related Items (1)

Learning latent and hierarchical structures in cognitive diagnosis models

Uses Software

• darch

Cites Work

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• A general method of empirical Q-matrix validation
• The generalized DINA model framework
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• Bayesian estimation of the DINA \(Q\) matrix
• Convergence of contrastive divergence algorithm in exponential family
• Identifiability of restricted latent class models with binary responses
• Partial identifiability of restricted latent class models
• Estimating the cognitive diagnosis \(Q\) matrix with expert knowledge: application to the fraction-subtraction dataset
• The sufficient and necessary condition for the identifiability and estimability of the DINA Model
• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Identifying Latent Structures in Restricted Latent Class Models
• Applying Test Equating Methods
• Justifying and Generalizing Contrastive Divergence
• Statistical Analysis ofQ-Matrix Based Diagnostic Classification Models

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