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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smoothbp is an R package implementing fast Bayesian hierarchical piecewise regression with logistic smoothing, random effects, and spike-and-slab breakpoint selection via a custom Metropolis-within-Gibbs sampler in Rust.
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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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smoothbp: Fast Bayesian Hierarchical Piecewise Regression with Smoothed Transitions and Spike-and-Slab Model Selection
smoothbp is an R package implementing fast Bayesian hierarchical piecewise regression with logistic smoothing, random effects, and spike-and-slab breakpoint selection via a custom Metropolis-within-Gibbs sampler in Rust.