Class of Fast Methods for Processing Irregularly Sampled or Otherwise Inhomogeneous One-Dimensional Data

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A 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 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 computationa...

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Class of fast methods for processing irregularly sampled or otherwise inhomogeneous one-dimensional data.

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ژورنال

عنوان ژورنال: Physical Review Letters

سال: 1995

ISSN: 0031-9007,1079-7114

DOI: 10.1103/physrevlett.74.1060