 A second proposed type of foundation for Bayesian epistemology is based on the idea of accurate estimation. This idea has two parts: estimation, and its accuracy. On this approach, one’s credence in a proposition A is one’s estimate of the truth value of A, where A’s truth value is identified with 1 if it is true and 0 if it is false (Jeffrey 1986). The closer one’s credence in A is to the truth value of A, the more accurate one’s estimate is. Then a Bayesian may argue that one’s credences ought to be probabilistic, for otherwise the overall accuracy of one’s credence assignment would be dominated—namely, it would, come what may, be lower than the overall accuracy of another credence assignment that one could have adopted. To some Bayesians, this gives a distinctively epistemic reason or explanation why one’s credences ought to be probabilistic. The result is the so-called accuracy-dominance argument for Probabilism (Joyce 1998). This approach has also been extended to argue for the Principle of Conditionalization (Briggs &amp; Pettigrew 2020). For more on this approach, see the entry on  epistemic utility arguments for probabilism  as well as Pettigrew (2016).