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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.ME2023
Valid and efficient imprecise-probabilistic inference with partial priors, III. Marginalization
Ryan Martin
As Basu (1977) writes, "Eliminating nuisance parameters from a model is universally recognized as a major problem of statistics," but after more than 50 years since Basu wrote thes…