Slowest adoption of Bayesian inference techniques ever …

So I have a candidate for the slowest adoption of basic ideas in Bayesian inference and it doesn’t come from astronomy … hooray!  In fact, this one comes from meta-analysis in clinical research; and in particular with regard to the use of likelihood functions.  In brief, O’Rourke and Altman (2005) have a section called “Even more flexibility for both — realization that one does not need common outcome scales or common summary statistics or anything other than a common parameter or common distribution of a parameter” in which they gush over the potential for fitting models to a mixed dataset of both continuous and censored/discretized observations of the same random variable through the amazing power of the likelihood function!  To quote from Curb Your Enthusiam, “LOL, Larry, L — O — L!”

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