**A**s Regina Liu gave her talk at ISI this morning on fusion learning and confidence distributions, this led me to think anew about this strange notion of confidence distributions, building a distribution on the parameter space without a prior to go with it, implicitly or explicitly, and vaguely differing from fiducial inference. (As an aside, the Wikipedia page on confidence distributions is rather heavily supporting the concept and was primarily written by someone from Rutgers, where the modern version was developed. [And as an aside inside the aside, Schweder and Hjort’s book is sitting in my office, waiting for me!])

Recall that a confidence distribution is a sample dependent distribution on the parameter space, which is uniform U(0,1) [in the sample] at the “true” value of the parameter. Used thereafter as a posterior distribution. (Again, almost always without a prior to go with it. Which is an incoherence from a probabilistic perspective. not mentioning the issue of operating without a pre-defined dominating measure. This measure issue is truly bothering me!) This seems to include fiducial distributions based on a pivot, unless I am confused. As noted in the review by Nadarajah et al. Moreover, the concept of creating a pseudo-posterior out of an existing (frequentist) confidence interval procedure to create a new (frequentist) procedure does not carry an additional validation *per se*, as it clearly depends on the choice of the initialising procedure. (Not even mentioning the lack of invariance and the intricacy of multidimensional extensions.)