a Bayesian interpretation where you don’t need to renormalize after every likelihood computation
How does this differ from using Bayes’ rule in odds ratio form? In that case you only ever have to renormalise if at some point you want to convert to probabilities.
How does this differ from using Bayes’ rule in odds ratio form? In that case you only ever have to renormalise if at some point you want to convert to probabilities.