  A useful sampling-reconstruction model should be stable with respect to
different kind of small perturbations, regardless whether they result from
jitter, measurement errors, or simply from a small change in the model
assumptions. In this paper we prove this result for a large class of sampling
models. We define different classes of perturbations and quantify the
robustness of a model with respect to them. We also use the theory of localized
frames to study the frame algorithm for recovering the original signal from its
samples.
