5 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
Induction and the rule of succession through a possibilistic inferential model lens
Ryan Martin, Shih-Ni Prim, Max Raner +1
Induction is the process by which empirical evidence is transformed to knowledge. Hume famously argued---and Popper and others agree---that there can be no logical justification fo…
Asymptotic efficiency of inferential models and a possibilistic Bernstein--von Mises theorem
Ryan Martin, Jonathan P. Williams
The inferential model (IM) framework offers an alternative to the classical probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. A key d…
Large-sample theory for inferential models: a possibilistic Bernstein--von Mises theorem
Ryan Martin, Jonathan P. Williams
The inferential model (IM) framework offers alternatives to the familiar probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. Allowing t…
Decision-making with possibilistic inferential models
Ryan Martin, Shih-Ni Prim, Jonathan Williams
Inferential models (IMs) are data-dependent, imprecise-probabilistic structures designed to quantify uncertainty about unknowns. As the name suggests, the focus has been on uncerta…