I'm using some bayesian hyperparameter optimization. I know how they works .
They always calculate the next values of the hyperparameter dependent on the result of former evaluations.
But what makes them bayesian? I know the bayesian theorem is:
p(A|B) = (P(B|A) * P(A)) / P(B)
What is the connection between this formula an the process of bayesian hyperparameter optimization? Is it just that the probability of a hyperparameter value depends on condition (former evaluations) we consider as relevant for the event?
Thanks for helping!