Cheung–Marks theorem

In information theory, the Cheung–Marks theorem, named after K. F. Cheung and Robert J. Marks II, specifies conditions where restoration of a signal by the sampling theorem can become ill-posed. It offers conditions whereby "reconstruction error with unbounded variance [results] when a bounded variance noise is added to the samples." == Background == In the sampling theorem, the uncertainty of the interpolation as measured by noise variance is the same as the uncertainty of the sample data when the noise is i.i.d.

Source: Wikipedia — Cheung–Marks theorem (CC BY-SA 4.0)

Cheung–Marks theorem

In information theory, the Cheung–Marks theorem, named after K. F. Cheung and Robert J. Marks II, specifies conditions where restoration of a signal by the sampling theorem can become ill-posed. It offers conditions whereby "reconstruction error with unbounded variance [results] when a bounded variance noise is added to the samples." == Background == In the sampling theorem, the uncertainty of the interpolation as measured by noise variance is the same as the uncertainty of the sample data when the noise is i.i.d.

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Source: Wikipedia "Cheung–Marks theorem" · CC BY-SA 4.0

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