A density-ratio framework compresses BMA posteriors into hard or soft support regions with explicit TV, KL, and predictive distortion bounds under predictor redundancy.
Prediction by Supervised Principal Components
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GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
citing papers explorer
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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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Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.