Bayesian conditionalization is rigid: learning \(E\) fixes p( \(E\) ) at 1 while preserving probabilities conditional on \(E\) . Non-rigid update is preferable when, in the course of learning that \(E\) is true, we change our views about how—by way of which truthmakers \(\epsilon\) . A Jeffrey-style generalization of Bayes—active conditioning—is developed which gives learning events a handle on \(\textrm{p}(\epsilon |E)\) and p( \(E\) ) both. \(E\) brings a truthmaker-incorporating “probasition” to the table, rather than simply an intension. Confirmation relations go hyperintensional as a result. \(E_{i}\) s true in the same worlds may not license the same updates, if their truth flows from different sources.
