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The Nyquist–Shannon sampling theorem is an essential principle for digital signal processing linking the frequency range of a signal and the sample rate required to avoid a type of distortion called aliasing. The theorem states that the sample rate must be at least twice the bandwidth of the signal to avoid aliasing.
The Whittaker–Shannon interpolation formula or sinc interpolation is a method to construct a continuous-time bandlimited function from a sequence of real numbers. The formula dates back to the works of E. Borel in 1898, and E. T. Whittaker in 1915, and was cited from works of J. M. Whittaker in 1935, and in the formulation of the Nyquist–Shannon sampling theorem by Claude Shannon in 1949.
The misconceived belief that the theorem applies to random sampling of any variable, rather than to the mean values (or sums) of iid random variables extracted from a population by repeated sampling. That is, the theorem assumes the random sampling produces a sampling distribution formed from different values of means (or sums) of such random ...
In either case, the value at x = 0 is defined to be the limiting value := = for all real a ≠ 0 (the limit can be proven using the squeeze theorem). The normalization causes the definite integral of the function over the real numbers to equal 1 (whereas the same integral of the unnormalized sinc function has a value of π ).
In probability theory, the optional stopping theorem (or sometimes Doob's optional sampling theorem, for American probabilist Joseph Doob) says that, under certain conditions, the expected value of a martingale at a stopping time is equal to its initial expected value. Since martingales can be used to model the wealth of a gambler participating ...
For band-limited functions, choosing the sampling rate > guarantees that no information is lost: since can be reconstructed from these sampled values. Then, by Fourier inversion, so can . This leads to the Nyquist–Shannon sampling theorem. [2]
In statistics, Cochran's theorem, devised by William G. Cochran, [1] is a theorem used to justify results relating to the probability distributions of statistics that are used in the analysis of variance.
sampling theory may mean: Nyquist–Shannon sampling theorem, digital signal processing (DSP) Statistical sampling; Fourier sampling This page was last edited on 30 ...