I'm graduated now, and swear I remember this exact kind of problem coming up in my Bayesian statistics class, but I can't remember what the answer was.
So my wife's brother is red/green colorblind (xY), but her father is not(XY), and her mother is not, so her mother must be a carrier(Xx). Red/green color-blindness is recessive on the X Chromosome(x), which means in order to have the phenotype (actually have it), all of your X chromosomes must have the gene(xx or xY). This means women must get it from both their mother and father, while men must get it from their mother.
As a result, my wife has a 50% chance of being a carrier(Xx or XX), while all of my sons have a 25% chance of being red/green colorblind(XY or xY).
What I'm trying to figure out is if my first son ends up NOT being red-green colorblind (XY), does that change the probability that my wife is a carrier, since I've now observed one datapoint that refutes that possibility? If so, by how much?
I think that Frequentists would say that it remains a 50% chance due to them being Independent events, but again, I can't recall exactly how it worked. I may be spacing it, but if my memory serves, Bayesians would include the original assumption(50%) as essentially a datapoint, but would subjectively give it more or less weight. I'm wondering if there's an objective way to handle this.
Of course, I can always calculate the odds of her being a carrier and how unlikely it is that she would have X sons without color-blindness (simple Geometric Distribution), but that doesn't tell me anything about her original probability.