Abstract

An alternative method for statistical interpolation is formalized. A new theorem is proved, providing theoretical basis for optimizing statistical accuracy in successively conditioned rendering applications. The theorem is empirically validated by two simulations, each comparing two different statistical interpolators. The interpolators are used to model high- resolution phase fluctuations over finite apertures. The theorem correctly predicts which interpolator is more optimal, based on empirical trials with greater than 99.9% certainty. The theorem is suitable as a quick alternative to the Monte Carlo optimization techniques used previously.

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