Safe Probability
This addresses foundational problems in statistics for researchers by providing a novel framework to unify and clarify probabilistic inference, though it appears incremental in building on existing concepts.
The paper formalizes 'safe probability' distributions that yield reliable predictions for specific aspects of a domain, offering a middle ground between imprecise/multiple-prior models and Bayesian approaches, and applies this to resolve issues like fiducial distributions and paradoxes such as Monty Hall.
We formalize the idea of probability distributions that lead to reliable predictions about some, but not all aspects of a domain. The resulting notion of `safety' provides a fresh perspective on foundational issues in statistics, providing a middle ground between imprecise probability and multiple-prior models on the one hand and strictly Bayesian approaches on the other. It also allows us to formalize fiducial distributions in terms of the set of random variables that they can safely predict, thus taking some of the sting out of the fiducial idea. By restricting probabilistic inference to safe uses, one also automatically avoids paradoxes such as the Monty Hall problem. Safety comes in a variety of degrees, such as "validity" (the strongest notion), "calibration", "confidence safety" and "unbiasedness" (almost the weakest notion).