Recovering a probabilistic knowledge structure by constraining its parameter space

DOI10.1007/s11336-008-9095-7zbMath1284.62748OpenAlexW2055646859MaRDI QIDQ1013055

Egidio Robusto, Luca Stefanutti

Publication date: 16 April 2009

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-008-9095-7

zbMATH Keywords

constrained parameter estimationbasic local independence modelprobabilistic knowledge structures

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (13)

Modeling misconceptions in knowledge space theory ⋮ An upgrading procedure for adaptive assessment of knowledge ⋮ Detecting and explaining BLIM's unidentifiability: forward and backward parameter transformation groups ⋮ Identifiability in probabilistic knowledge structures ⋮ BLIM's identifiability and parameter invariance under backward and forward transformations ⋮ Assessment-based correct rates in learning spaces ⋮ Representing probabilistic models of knowledge space theory by multinomial processing tree models ⋮ Considerations about the identification of forward- and backward-graded knowledge structures ⋮ On the unidentifiability of a certain class of skill multi map based probabilistic knowledge structures ⋮ Assessing parameter invariance in the BLIM: bipartition models ⋮ Markov solution processes: modeling human problem solving with procedural knowledge space theory ⋮ A correct response model in knowledge structure theory ⋮ Uncovering the best skill multimap by constraining the error probabilities of the gain-loss model

Cites Work

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• A generalization of knowledge space theory to problems with more than two answer alternatives
• Estimating multiple classification latent class models
• Thevenin decomposition and large-scale optimization
• Spaces for the assessment of knowledge
• Knowledge Spaces
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Feature‐Specific Penalized Latent Class Analysis for Genomic Data
• A class of stochastic procedures for the assessment of knowledge

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