The problem of interpolating a sampled signal is well known in signal processing. We describe a Bayesian interpolator that models the sampling process and the autocovariances of both the signal and the noise in its design. We show that the technique is an adaptation of the kriging process used in geostatistics, and how it can be applied to interpolating aliased sampled signals.
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