He also explains that easy analytical solutions often don't exists to seemingly easy questions, such as this one. That's why Bayesian statistics needs Monte-Carlo.
He also explains that easy analytical solutions often don't exists to seemingly easy questions, such as this one. That's why Bayesian statistics needs Monte-Carlo.
What I'm asking is: how do you find parameters for A and B given "P(B > A) = 0.71"?
About the actual question, I don't think that's very well specified in this form. You could have A~Beta0 where Beta0 is very concentrated around let's say 0.5, and then find a B~Beta1 where Beta1 has 71% of its weight above 0.5. That would be a pretty good approximation for it, in fact as close as you want if you increase sample size of A. I can't verify this, but I think finding Beta1 should be easy. Obv this is not what you are looking for though, because this would require A to have a much larger sample size than B. I guess we should add some restriction, like similar sample sizes.