> A good book that talk about this which I've been meaning to read is... In All Likelihood: Statistical Modelling and Inference Using Likelihood by Yudi Pawitan

> A good book that talk about this which I've been meaning to read is... In All Likelihood: Statistical Modelling and Inference Using Likelihood by Yudi Pawitan. It's good but it's mostly a technical introduction to statistical inference (i.e. the mathematics that make statistics work), and the remarks about the differences between different schools of statistics are mostly short asides. It's not a great recommendation for non-statisticians who want more insight into the different schools of statistics. Also, both frequentist and Bayesian methods rely on likelihoods as an intermediary, so I guess you could say it's a "compromise" but only in a very uninteresting way. The likelihood L(theta|data) is simply shorthand for P(data|theta), and to get a posterior probability you then simply apply Bayes' theorem to get P(theta|data) ~ P(data|theta) * P(theta), and this last part is your prior.