You should put priors on the parameter values of a model, not its truth&#x2F;falsehood. I have no idea where the latter idea comes from. There may be something to it but it sounds nonsensical to me.<p>For example, a model of carcinogenesis could use a prior for mutations rates centered on around 10^-6 somatic mutations&#x2F;base-pair&#x2F;cell division rather than 10^-100 or 10^100. The first is too rare and second too common to be consistent with other lines of evidence.<p>The priors don&#x27;t have to be exact, just in the right ballpark to constrain the model output to realistic scenarios. Of course you could argue about what&#x27;s a realistic mutation rate, just like you could argue about the choice of model itself (maybe some people believe somatic mutations are a result rather than cause of cancer, so including those values in the model is wrong to begin with).