I begin by discussing a conundrum that arises when Bayesian models attempt to assess the relevance of one claim to another. I then explain how my formal modeling framework (the ''Certainty Loss Framework'') manages this conundrum. Finally, I apply my modeling methodology to respond to Namjoong Kim's objection to my framework.In my (2008) and (2013), I developed a formal framework (the ''Certainty-Loss Framework'', or ''CLF'') for building models of stories in which agents assign degrees of belief to claims over time. These models can be used to answer a variety of questions about such stories: Do the confidence assignments described in the story violate particular rational rules? What degrees of belief should the agent assign besides those explicitly described in the story? Should the agent's confidence in particular claims change over time? At a given time, which pieces of information should the agent consider relevant to a particular claim?One great advantage of Bayesian modeling frameworks is their ability to assess relevance among claims. On the Bayesian approach, one claim is relevant to a second claim for a given agent at a given time just in case learning the first claim at that time would rationally require the agent to change her degree of belief in the second claim. With this test for relevance in hand, the Bayesian can answer a synchronic question about relevance by investigating a diachronic question concerning attitude change.