Information-feedback control schemes (more specifically, sensor-based control schemes) select an action at each stage based on the sensory data provided at that stage. Since it is impossible to know future sensor readings in advance, predicting the future behavior of a system becomes difficult. Hyper-particle filtering is a sequential computational scheme that enables probabilistic evaluation of future system performance in the face of this uncertainty. Rather than evaluating individual sample paths or relying on point estimates of state, hyper-particle filtering maintains at each stage an approximation of the full probability density function over the belief space (i.e., the space of possible posterior densities for the state estimate). By applying hyper-particle filtering, control policies can be more more accurately assessed and can be evaluated from one stage to the next. These aspects of hyper-particle filtering may prove to be useful when determining policies, not just when evaluating them.