Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Projective inference in high-dimensional problems
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A density-ratio framework compresses BMA posteriors into hard or soft support regions with explicit TV, KL, and predictive distortion bounds under predictor redundancy.
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
citing papers explorer
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To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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Bayesian Model Averaging under Predictor Redundancy via Density-Ratio Posterior Compression
A density-ratio framework compresses BMA posteriors into hard or soft support regions with explicit TV, KL, and predictive distortion bounds under predictor redundancy.
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Bayesian Modeling and Prediction of Generalized Contact Matrices
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.