&gt; I&#x27;m also not sure why the OP thinks that calculating the normalizing constant is a huge issue. Most of the time you rarely need it since you&#x27;re likely going to end up doing an MCMC or some other sampling method for the posterior, in which case you only need proportionality.<p>Right, and when doing Bayesian model selection the evidence drops out of the problem completely. All you need are the priors and the likelihoods.<p>The claim that ~H is &quot;not itself a valid hypothesis&quot; is dubious. If H is the hypothesis that a certain continuous parameter has a value in the range [a, b], then it&#x27;s perfectly obvious what ~H means.<p>Of course it&#x27;s possible to choose a vague, overly broad hypothesis, but a frequentist analysis of such a hypothesis is going to be just as bad as a Bayesian analysis. &quot;Garbage in, garbage out&quot; is true no matter what tool you use.