Flag - suggested answer (don't read if don't want to see a (possibly incorrect) attempt:

Grateful for comments here as I am not very certain on the situations that the MLE approach is better vs situations where Bayesian approach is better

Suggested answer:

c(i) Is frequentist approach where we have one parameter estimate (the MLE) c(ii) bayesian approach - distribution over parameters and we update our prior belief based on observations If we have no prior belief - c(i) may be a better estimate (i.e. in (my version of) c(ii) we are constraining the parameters to be 0.7 or 0.2 and updating our relative convictions about these - which is a strong prior asssumption (we can never have 0.5 for instance) If we do have prior belief and also want to incorporate uncertainty estimations in our parameters, I think c(ii) is better If the MLE is 0.7 then we will have c(i) giving 0.7 and c(ii) giving 0.7 with a very high probability and 0/2 with a very low probability to the methods will perform similarly