canonical dataset of observations [...] unreliable prior should gradually be diluted
Indeed, if you have enough observations then the prior eventually doesn't matter. The difficulty is in the selection of the observations. Ideally you should include every potentially relevant observation -- including, e.g., every time someone looks up at the sky and doesn't see an alien spaceship, and every time anyone operates a radar or a radio telescope or whatever and sees nothing out of the ordinary.
In practice it's simply impractical to incorporate every potentially relevant observation into our thinking. But that makes it awfully easy to have some bias in selection, and that can make a huge difference to the conclusions.
In practice it's simply impractical to incorporate every potentially relevant observation into our thinking. But that makes it awfully easy to have some bias in selection, and that can make a huge difference to the conclusions.
Yes these circumstances induce bias and this is unfortunate if one wants to say anything about frequency and such things.
Another somewhat simpler question is this: given n observations of something the observer thinks is a UAP, what is the probability that at least one of these observations originated from a UAP?
If for each of the...
It would be a powerful tool to be able to dismiss fringe phenomena, prior to empirical investigation, on firm epistemological ground.
Thus I have elaborated on the possibility of doing so using Bayes, and this is my result:
Using Bayes to dismiss fringe phenomena
What do you think of it?