ArticlePsychometrika2023
Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.
Article in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 13 citations in OpenAlex.
- Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.Psychometrika · 2025Article
- Bayesian Transition Diagnostic Classification Models with Polya-Gamma Augmentation.Psychometrika · 2025Article
- Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
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Abstract
Hidden Markov models (HMMs) have been applied in various domains, which makes the identifiability issue of HMMs popular among researchers. Classical identifiability conditions shown in previous studies are too strong for practical analysis. In this paper, we propose generic identifiability conditions for discrete time HMMs with finite state space. Also, recent studies about cognitive diagnosis models (CDMs) applied first-order HMMs to track changes in attributes related to learning. However, the application of CDMs requires a known [Formula: see text] matrix to infer the underlying structure between latent attributes and items, and the identifiability constraints of the model parameters should also be specified. We propose generic identifiability constraints for our restricted HMM and then estimate the model parameters, including the [Formula: see text] matrix, through a Bayesian framework. We present Monte Carlo simulation results to support our conclusion and apply the developed model to a real dataset.
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