We have seen the Bayesian schema inform us about how to update our beliefs, and the likelihood analysis schema disregard the priors and instead tells us about relative evidential strength.

The Bayes factor assisted you in optimal guesswork: how much you should, subjectively, favor one hypothesis over the other, in light of the available evidence.

A Bayesian analysis of the data from a simple experimental design is straightforward. Recall that the theorem implies that the posterior is the likelihood weighted by the prior…

What, then, are the parameters of the normal distribution? An idealized, normal distribution is fully determined given two quantities: its midpoint and its width.