The prior can generally only be understood in the context of the likelihood
Nice article, in the sense that I would assign it to students, although I think it presents sort of a strawman and doesn&#x27;t really introduce anything that hasn&#x27;t been discussed at length elsewhere.<p>It provides a nice introduction to types of objective priors, hinting at their advantages and disadvantages. Also a nice discussion of why priors matter. Incidentally, I agree that &quot;subjective&quot; and &quot;objective&quot; are poor labels for priors--something better would be something like &quot;estimand-predictive&quot; and &quot;inferential-property&quot; priors, respectively.<p>I&#x27;m not really sure why they suggest objective priors violate assumptions about not using information from the data. The model, and thus many objective priors, can be specified given the design, without any knowledge of what the data looks like. This is the strawman part of it.<p>I get the sense Gelman has been wrestling with objective priors the last few years.