2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
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…
Computationally efficient variational-like approximations of possibilistic inferential models
Leonardo Cella, Ryan Martin
Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficien…
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…