This dissertation is an elaboration and defense of probabilism, the view that belief comes in various degrees of strength, and that the probability calculus provides coherence norms for these degrees of belief. Probabilism faces several well-known objections. For example, critics object that probabilism’s numerical representation of degrees of belief is too precise, and that its coherence norms are too demanding for real human agents to follow. While probabilists have developed several plausible responses to these objections, the compatibility among these responses is unclear. On this basis, I argue that probabilists must articulate unified methodological and normative foundations for their view, and I sketch the foundations of a probabilist modeling framework, the Comparative Confidence Framework (CCF). CCF characterizes probabilism primarily as an account of ideal degree of belief coherence. CCF provides a set of fundamentally qualitative and comparative—rather than quantitative—evaluative ideals for degree of belief coherence. By providing qualitative, comparative, evaluative coherence norms for degrees of belief, CCF avoids the aforementioned objections: that probabilism’s formal representation of degrees of belief is too precise, and that its norms are too demanding. CCF is a first step in the development of unified foundations for a wider subjectivist Bayesian theory of doxastic and pragmatic rationality.