They aren't the ones that are problems. The one who doesn't fit the norm. While I'm in no way a statistics expert, my understanding of Bayes Theorem is that it provides a model that responds to new data. This is a very big plus. After dosing several hundred patients with aminoglycosides over the past couple of decades, using everything from natural log tables and a piece of paper to programs that use Baysean modeling, I'll stick with what I've got until something better comes along.
Results are what's important.
The bottom line for where I practice is getting the patient well and causing as little harm along the way as possible. The elegance of research is a bit beyond me. I'll stick with afebrile patients with intact kidneys along with any other analogous situations I can help provide. Hence the solution to the Bayesian problem will be an average over the whole course. This is a way to differentiate the points to fit taking information about the analitical technique, for example.