A new fully conjugate Bayesian variable selection method for GLMs with posterior consistency guarantees for inclusion indicators and coefficients, implemented via Gibbs sampling.
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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.
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.
Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
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.
citing papers explorer
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Bayesian Variable Selection in Generalized Linear Models
A new fully conjugate Bayesian variable selection method for GLMs with posterior consistency guarantees for inclusion indicators and coefficients, implemented via Gibbs sampling.
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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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Statistical Tapers for Correlation-Based Localization in Ensemble Data Assimilation
Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
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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.