A conditional Bayesian latent variable model with tailored Gibbs sampling improves variable selection and efficiency when relating ordinal academic performance to continuous self-efficacy.
arXiv preprint arXiv:2505.10099 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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DASH reduces problem dimensionality in MIQP subset selection to improve MIP solver incumbent quality on hard portfolio instances.
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How does academic performance affect self-efficacy? Interpretable modelling through latent academic achievement
A conditional Bayesian latent variable model with tailored Gibbs sampling improves variable selection and efficiency when relating ordinal academic performance to continuous self-efficacy.
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DASH: A Dimensionality Reduction Method for Large-scale Convex MIQP with Applications in Subset Portfolio Selection
DASH reduces problem dimensionality in MIQP subset selection to improve MIP solver incumbent quality on hard portfolio instances.