For a wonderful biographical take on this topic try "The theory that would not die-how Bayes'rule cracked the Enigma code, hunted down Russian submarines & emerged triumphant from centuries of controversy" by Sharon McGrayne.<p>She tells a terrific story with a fascinating large cast of characters including Laplace,Bayes,Fisher,Pearson,Jeffries,Savage,Turing and many others. Engagingly told, highly recommended. Could the takeaway "Do you want to solve a practical problem or do you want scientific rigor?"
For anyone looking for a quick and hands-on dive into the world of Bayesian modelling and inference, I can't recommend JASP enough, made freely available by the University of Amsterdam[0]. I've recommended it before, and it's just a breeze to work with, seeing frequentist and Bayesian analyses side-by-side.<p>[0]: <a href="https://jasp-stats.org/" rel="nofollow">https://jasp-stats.org/</a>
This SEP article isn't bad, but a better philosophical introduction to Bayesian epistemology is Jonathan Weisberg's "Varieties of Bayesianism" for the Handbook of the History of Logic:<p><a href="https://jonathanweisberg.org/pdf/VarietiesvF.pdf" rel="nofollow">https://jonathanweisberg.org/pdf/VarietiesvF.pdf</a><p>Another point to notice is that "Bayesian epistemology" generally means "Bayesianism as discussed by philosophers". There is also "Bayesian statistics", i.e. as discussed by statisticians. Introductions to either of them show surprisingly little overlap between the two "Bayesianisms".
Going to leave this here: <a href="https://plato.stanford.edu/entries/popper/#GrowHumaKnow" rel="nofollow">https://plato.stanford.edu/entries/popper/#GrowHumaKnow</a>
There’s no “prior” to the priors, thus the priors are actually posteriors, and follow from habit and not anything deductively necessary. Habit always evades critique, as it explains itself. Thus, we will be forced, ceaselessely, to think in the same ways, just with differing parameters.