Formal theories of belief-credence interaction that satisfy the standard logical requirements on belief, such as conjunctive closure, face the problem of partition-sensitivity. According to these theories, a rational agent can believe \(X\) relative to one partitioning of possibilities, but the same belief may not be rational relative to some other partitioning, even when the agent’s evidence remains the same. Focusing on Leitgeb’s stability theory (Leitgeb, The stability of belief: How rational belief coheres with probability, Oxford University Press, 2017), which exemplifies this problem, this paper aims to go beyond the simple partition-sensitivity of such theories. To address this and related problems, I connect the issues surrounding belief-credence interaction with Bayesian network theory and the notion of epistemic basing. The view that I defend and explicate is that rational belief corresponds to stably high probability within a network of interconnected propositions, where the network is structured by the relation of direct explanatory priority. As I argue, this view provides solutions to important problems regarding partition-sensitivity, specifically the problems involving irrelevant propositions and large partitions. In the final section, I extend the proposed theory and articulate the network-based notion of robustness of belief, which differentiates lottery-type beliefs from more ordinary beliefs.
