To select or not to select?

This post is by Aki New preprint To select or not to select: predictively consistent priors instead of model selection with Anna Elisabeth Riha, Leevi Lindgren, David Kohns, Paul Bürkner and me. arXiv.2606.22850 tl;dr: Model selection is not a substitute for building good models in the first place. Abstract: Bayesian modelling workflows often consider multiple candidate models of varying complexity. Model selection is commonly used to navigate potential trade-offs between model complexity and generalisability to new data. We study when model selection is unnecessary or can even be harmful for predictive performance in finite data regimes and find that the need for selecting simpler models can depend on prior choice. We formalise predictively consistent priors, which keep prior predictive implications stable as model complexity increases. Across examples and numerical experiments, including adding covariates in linear and logistic regression, forward variable selection, and nonlinear…