You are correct: the way these mathematical creatures work, plausibilities behave like probabilties. What tjradcliife is trying to do, by differentiating betwee

You are correct: the way these mathematical creatures work, plausibilities behave like probabilties. What tjradcliife is trying to do, by differentiating between plausibilities and probabilities is to address the concern of frequentism with Bayesianism: namely, the "P(h)"-- what is usually termed as a "prior probability" in statistical jargon-- usually does not arise from objective data. Accordingly, Bayesianism can be treated with suspicion-- since the P(h) is arbitrarily specified, one could "load the dice" and obtain a P(h|e) ("posterior probability") guaranteed to suit the analyst's conclusions. By differentiating the two creatures, tjradcliffe hopes to resolve the dis-ease with calling P(h|e) a "probability". By frequentist philosophy, probabilities should be grounded in some sort of firm soil, e.g., the relative frequency of what would be observed if one could conduct an experiment ad infinitum. A Bayesian "probability" is not grounded this way, so calling it a "plausibility" instead would resolve this tension. Another way of putting this-- P(h) to a frequentist feels like a tainted probability-- really not a probability at all-- so even after updating the prior probability with data [i.e., P(e|h)/P(e)], the posterior probability P(h|e) still retains the influence of that original taint. Solution: stop calling the Bayesian probabilities "probabilities"; instead, call them "plausibilities".