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"Sampling Theory" as a book category encompasses works that explore the principles, methods, and applications of converting continuous or large-scale phenomena into discrete, representative measurements and then recovering, analyzing, or making inferences from those samples. It includes classical signal-processing topics (Nyquist–Shannon sampling theorem, aliasing, interpolation/reconstruction, multirate systems, quantization, and filter design), modern advances (compressed sensing, sparse sampling, and sampling on graphs/manifolds), and related statistical survey and experimental-design methods (random/probability sampling, estimation, and error analysis). Books in this category mix mathematics (Fourier analysis, probability, linear algebra), computational algorithms, and practical concerns (hardware, noise, sensor/measurement constraints) and serve audiences ranging from engineers and applied mathematicians to statisticians and data scientists working in communications, imaging, audio, remote sensing, and big-data sampling problems.

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