Likehood or probability makes sense only in models where one knows with maximum certainty that he have captured <i>all&#x2F;every</i> relevant variables,  its weights and has all kinds of possible events in a distribution. Otherwise the whole model is mere a story. An illusion. Failure to capture reality adequately is where so-called &quot;black swans&quot; are coming from.<p>This is meaning behind the &quot;correlation is not causation&quot; meme. There is nothing wrong with Bayesian reasoning, except when it is applyed to an inadequate dataset, which is almost always the case.<p>Would you like to elaborate about &quot;somewhat wrong&quot;, with quotations from Principles of Mathematics, for example?