RA Fisher, the father of modern statistics, proposed the idea of fiducial inference during the
first half of the 20th century. While his proposal led to interesting methods for quantifying …

Posterior probabilistic statistical inference without priors is an important but so far elusive
goal. Fisher's fiducial inference, Dempster–Shafer theory of belief functions, and Bayesian …

Between the two dominant schools of thought in statistics, namely, Bayesian and
classical/frequentist, a main difference is that the former is grounded in the mathematically …

Statistics has made tremendous advances since the times of Fisher, Neyman, Jeffreys, and
others, but the fundamental and practically relevant questions about probability and …

Bayesian inference requires specification of a single, precise prior distribution, whereas
frequentist inference only accommodates a vacuous prior. Since virtually every real-world …

The inferential model (IM) framework provides valid prior-free probabilistic inference by
focusing on predicting unobserved auxiliary variables. But, efficient IM-based inference can …

Prediction, where observed data is used to quantify uncertainty about a future observation, is
a fundamental problem in statistics. Prediction sets with coverage probability guarantees are …

The development of statistical methods for valid and efficient probabilistic inference without
prior distributions has a long history. Fisher's fiducial inference is perhaps the most famous …

Prediction of future observations is an important and challenging problem. The two
mainstream approaches for quantifying prediction uncertainty use prediction regions and …

Between Bayesian and frequentist inference, it's commonly believed that the former is for
cases where one has a prior and the latter is for cases where one has no prior. But the …