A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
a registered report testing the effect of sleep on drm false memory: Greater lure and veridical recall but fewer intrusions after sleep
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Mixtures of mutually singular distributions let Bayesian variable selection run in ordinary fixed-dimensional MCMC while matching the spike-and-slab posterior and RJMCMC acceptance probabilities.
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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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Reversible Jump MCMC With No Regrets: Bayesian Variable Selection Using Mixtures of Mutually Singular Distributions
Mixtures of mutually singular distributions let Bayesian variable selection run in ordinary fixed-dimensional MCMC while matching the spike-and-slab posterior and RJMCMC acceptance probabilities.
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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.