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An adaptive agent predicting the future state of an environment must weightrust in new observations against prior experiences. In this light, we proposea view of the adaptive immune system as a dynamic Bayesian machinery thatupdates its memory repertoire by balancing evidence from new pathogenencounters against past experience of infection to predict and prepare forfuture threats. This framework links the observed initial rapid increase of thememory pool early in life followed by a mid-life plateau to the ease oflearning salient features of sparse environments. We also derive a modulatedmemory pool update rule in agreement with current vaccine response experiments.Our results suggest that pathogenic environments are sparse and that memoryrepertoires significantly decrease infection costs even with moderate sampling.The predicted optimal update scheme maps onto commonly considered competitivedynamics for antigen receptors.

How a well-adapting immune system remembers