Proposes a Bayesian pipeline for clinical prediction models with shrinkage priors, Laplace/normal approximations, and posterior mean computations via quadrature or projection-predictive mapping to improve uncertainty quantification and clinical utility over plug-in methods.
Kass and Larry Wasserman
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Progression to the mean: A practical Bayesian workflow for the development and deployment of clinical prediction models
Proposes a Bayesian pipeline for clinical prediction models with shrinkage priors, Laplace/normal approximations, and posterior mean computations via quadrature or projection-predictive mapping to improve uncertainty quantification and clinical utility over plug-in methods.