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.
A survey of Bayesian predictive methods for model assessment, selection and comparison
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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.
citing papers explorer
-
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.
-
Improved prediction of extreme random effects in joint models: WRaPs
WRaPs extends optimally weighted random effect estimators to joint models, providing closed-form solutions for basic cases and MCMC computation for complex ones to predict extreme random effects while accounting for survival data.
-
Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
-
Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
-
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.