Learn Optimal Parameters of Beta Distribution for Bayesian A/B Test What are the known / efficient methods to learn the parameters of a Beta Distribution in the context of A/B testing?
Is it a reasonable starting point to learn from the history of the base (Let's say form A data which is the base variant)?

Assuming you're using the Beta Binomial model, a way to do it is to update the posterior with the data.
It can be done by the formula of the conjugate:

Image from Wikipedia - Conjugate Prior.
