Quantitative $ L ^{ 2 } $ Approximation Error of a Probability Density Estimate Given by It Samples

We present a new result characterized by an exact integral expression for the approximation error between a probability density and an integer shift invariant estimate obtained from its samples. Unlike the Parzen window estimate, this estimate avoids recomputing the complete probability density for each new sample: only a few coefficients are required making it practical for real-time applications. We also show how to obtain the exact asymptotic behavior of the approximation error when the number of samples increases and provide the trade-off between the number of samples and the sampling step size.


Published in:
Proceedings of the Twenty-Ninth IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Montréal QC, CA, 952–955
Year:
2004
Publisher:
IEEE
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

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