Model Choice Workshop

Bayesian inference is often said to begin with a prior, yet an even earlier step—the articulation of a statistical model—quietly fixes the outcome space, the likelihood, the data representation, and the structural assumptions that make inference possible. This workshop asks whether and how that step can be principled or partly formalized: what counts as a good modeling choice? How can simplicity and expressiveness be negotiated given the available evidence? Bringing together perspectives, we will compare the rationales and heuristics different communities use when specifying models, identify common ground and points of divergence, and work through concrete cases that reveal the practical stakes of "prior to the prior" decisions. The aim is to distill a shared vocabulary for reasoning about model choice and equip participants with questions and habits that make early modeling decisions more transparent and effective.