A Class of Fast Methods for Processing Irregularly Sampled or Otherwise Inhomogeneous One-Dimensional Data
نویسنده
چکیده
With the ansatz that a data set’s correlation matrix has a certain parametrized form (one general enough, however, to allow the arbitrary specification of a slowly-varying decorrelation distance and population variance) the general machinery of Wiener or optimal filtering can be reduced from O(n) to O(n) operations, where n is the size of the data set. The implied vast increases in computational speed can allow many common sub-optimal or heuristic data analysis methods to be replaced by fast, relatively sophisticated, statistical algorithms. Three examples are given: data rectification, highor lowpass filtering, and linear least squares fitting to a model with unaligned data points. PACS numbers: 06.50.-x, 02.50.Rj, 02.50.Vn Typeset using REVTEX
منابع مشابه
Class of fast methods for processing irregularly sampled or otherwise inhomogeneous one-dimensional data.
With the ansatz that a data set’s correlation matrix has a certain parameterized form (one general enough, however, to allow the arbitrary specification of a slowly-varying decorrelation distance and population variance) the general machinery of Wiener or optimal filtering can be reduced from O(n) to O(n) operations, where n is the size of the data set. The implied vast increase in computationa...
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