Been reading a lot of Gelman, Carlin, Stern, Dunson, Vehtari, Rubin “Bayesian Data Analysis” 3rd edition lately. Overall in the Bayesian framework some ideas (such as regularization, and imputation) are way easier to justify (though calculating some seemingly basic quantities becomes tedious). A big advantage (and weakness) of this formulation is statistics has a much less “shrink wrapped” feeling than the classic frequentist presentations. You feel like the material is being written to peers instead of written to calculators (of the human or mechanical variety). In the Bayesian formulation you don’t feel like you will be yelled at for using 1 tablespoon of sugar when the recipe calls for 3 teaspoons (at least if you live in the United States).
Some other stuff reads differently after this though. Continue reading Skimming statistics papers for the ideas (instead of the complete procedures)