Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
Statistics and computing , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A hierarchical INLA approach decomposes non-linear biomarker scaling in joint longitudinal-survival models into a parametric baseline and data-driven smooth deviation via second-order random walk basis, enabling fast inference and linearity checks.
Simulation comparison finds bulgeless galaxies host more centrally concentrated, disc-aligned satellites with steeper faint-end luminosity functions than bulge-dominated controls, reflecting co-evolution and quieter merger histories.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
citing papers explorer
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Boundedly Rational Meta-Learning in Sequential Consumer Choice
Consumers transfer brand-level regularities across contexts using low-D boundedly rational meta-learning approximations that fit choice data better than no-transfer or fully integrated Bayesian benchmarks.
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Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
A hierarchical INLA approach decomposes non-linear biomarker scaling in joint longitudinal-survival models into a parametric baseline and data-driven smooth deviation via second-order random walk basis, enabling fast inference and linearity checks.
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Bulgeless Evolution And the Rise of Discs (BEARD) III. A numerical simulation view of satellites around Milky-Way analogues
Simulation comparison finds bulgeless galaxies host more centrally concentrated, disc-aligned satellites with steeper faint-end luminosity functions than bulge-dominated controls, reflecting co-evolution and quieter merger histories.
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A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.