I would advise looking into frequentist statistics before studying Bayesian statistics.
Actually, if you have the necessary math background, it will probably be useful to start by looking at why and how the frequentists and the Bayesians differ.
Thanks for pointing out the Gelman and Shalizi paper. Just skimmed it so far, but it looks like it really captures the zeitgeist of what reasonably thoughtful statisticians think of the framework they’re in the business of developing and using.
Plus, their final footnote, describing their misgivings about elevating Bayesianism beyond a tool in the hypothetico-deductive toolbox, is great:
Ghosh and Ramamoorthi (2003, p. 112) see a similar attitude as discouraging inquiries into consistency: ‘the prior and the posterior given by Bayes theorem [sic] are imperatives arising out of axioms of rational behavior – and since we are already rational why worry about one more’ criterion, namely convergence to the truth?
Actually, if you have the necessary math background, it will probably be useful to start by looking at why and how the frequentists and the Bayesians differ.
Some good starting points, in addition to Bayes, are Fisher information and Neyman-Pearson hypothesis testing. This paper by Gelman and Shalizi could be interesting as well.
Thanks for pointing out the Gelman and Shalizi paper. Just skimmed it so far, but it looks like it really captures the zeitgeist of what reasonably thoughtful statisticians think of the framework they’re in the business of developing and using.
Plus, their final footnote, describing their misgivings about elevating Bayesianism beyond a tool in the hypothetico-deductive toolbox, is great: