 Jeffrey conditionalization says that the agent’s new subjective probability for \(B\), after her subjective probabilities for the elements of the partition have changed to \(p_{i}\), should equal the weighted sum of her old subjective probabilities for \(B\) conditional on the \(A_{i}\), where the weights are the new subjective probabilities \(p_{i}\) for the elements of the partition.