Validation is concerned with extrapolation away from historical records. A naive statistical perspective would view this problem as one of characterizing outliers, settling for significant errors associated with approximating and sampling rare events. A hierarchical perspective, however, quickly regularizes the validation problem with constraints that, while not visible at the operational scale, can be credibly transferred from other contexts such as laboratory experiments or other operational settings. Scaling is a key challenge with this operation of knowledge transfer. It pertains to clarifying intrinsic structure that is invariant across data items acquired under different conditions. In this talk I will describe recent efforts at combining hierarchical models with intrinsic structure paradigms for the purpose of out-of-set prediction.