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 )
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
background 1polarities
unclear 1representative citing papers
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.
A cost-sensitive trigger using specification debt for deciding when to re-specify forecasting model forms, shown on M4 data to match full-update accuracy at 28% of the compute cost.
citing papers explorer
-
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.