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.
Bayesian inference with the l1-ball prior: solving combinatorial problems with exact zeros
2 Pith papers cite this work. Polarity classification is still indexing.
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A joint Bayesian framework with sparsity priors for simultaneous inference of NLMEM parameters and high-dimensional genetic covariate selection in population pharmacokinetics.
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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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Joint Bayesian Inference of Genetic Effect Sizes and PK Parameters in Nonlinear Mixed-Effects Models
A joint Bayesian framework with sparsity priors for simultaneous inference of NLMEM parameters and high-dimensional genetic covariate selection in population pharmacokinetics.