 So, on the above view, idealization is essential to the existing applications of Bayesian epistemology in science. If so, the real issue is whether the kind of scientific inquiry empowered by Bayesian idealization serves the purpose of the inferential task better than do the non-Bayesian rivals, such as so-called frequentism and likelihoodism in statistics. For a critical comparison of those three schools of thought about statistical inference, see Sober (2008: ch. 1), Hacking (2016), and the entry on  philosophy of statistics.  For an introduction to both Bayesian statistics and frequentist statistics written for philosophers, see Howson &amp; Urbach (2006: ch. 5–8).