The problem with calling parts of a learning algorithm a prior that are not free variables, is that then anything (every part of any learning algorithm) would count as a prior. So even the Bayesian conditionalization rule itself. But that’s not what Bayesians consider part of a prior.
The problem with calling parts of a learning algorithm a prior that are not free variables, is that then anything (every part of any learning algorithm) would count as a prior. So even the Bayesian conditionalization rule itself. But that’s not what Bayesians consider part of a prior.