2 papers
stat.ME2025
No-prior Bayes reIMagined: probabilistic approximations of inferential models
Ryan Martin
When prior information is lacking, the go-to strategy for probabilistic inference is to combine a "default prior" and the likelihood via Bayes's theorem. Objective Bayes, (generali…
stat.CO2025
An efficient Monte Carlo method for valid prior-free possibilistic statistical inference
Ryan Martin
Inferential models (IMs) offer prior-free, Bayesian-like posterior degrees of belief designed for statistical inference, which feature a frequentist-like calibration property that…