I'm looking for an intuition, apologies for the non-technical language.
In Bayesian Learning one starts from prior knowledge and incorporates a new fact to obtain posterior knowledge.
What is the weight of the prior knowledge VS the weight of the new fact, in determining the posterior knowledge?
On one extreme, I could totally change my mind at any new fact. if my prior knowledge says 1, and the fact says 2, then my posterior knowledge will say 2
On the other extreme, I could be stubborn and hardly change my mind in face of new facts. If my prior knowledge says 1 and the new fact says 2, my posterior knowledge will say 1.001
Is there a parameter that regulates how quickly I change my mind in face of new facts?