Where the Bayesian and Frequentist Approaches Meet

Sebastian Sauer bridges the gap:

However, a disadvantage of Bayes analysis, at least at its current state, is that it has higher technical and computational demands. For beginners in particular, this may present a substantial (entry) burden. Teaching statistics, I have found that students (and many colleagues) have had difficulties installing Stan (particularly the C++ compiler needed in order to run Stan); Stan is the probabilistic programming language which many front-end Bayes engines use such as brms in R.

Thus, the installation process being not so user-friendly, a burden is placed for beginners which may prevent using Bayes methods.

In that light, this post explores the numerical simarilities of Bayes regression models and Frequentis models. The idea is to use a Frequentist regression model as a proxi for a full Bayesian analysis. The value added is the quick computation and the simple technical setup.

Click through for the conditions where you’ll find very similar results, as well as a few examples of it in action.