Derives closed-form solutions including scalar inverse-gradient density formulas for f-divergence, Bregman, and Rényi penalized variational problems at the measure level.
Using Stacking to Average Bayesian Predictive Distributions (with Discussion )
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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.
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
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Closed-form solutions to some generalized variational inference problems
Derives closed-form solutions including scalar inverse-gradient density formulas for f-divergence, Bregman, and Rényi penalized variational problems at the measure level.
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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 Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.