Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/8130
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dc.contributor.authorRajan, A.
dc.contributor.authorRao, A.
dc.contributor.authorVittal, Rao, R.
dc.contributor.authorJamadagni, H.S.
dc.date.accessioned2020-03-30T10:18:06Z-
dc.date.available2020-03-30T10:18:06Z-
dc.date.issued2014
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2014, Vol.264, , pp.213-224en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8130-
dc.description.abstractThe need for generating samples that approximate statistical distributions within reasonable error limits and with less computational cost, necessitates the search for alternatives. In this work, we focus on the approximation of Gaussian distribution using the convolution of integer sequences. The results show that we can approximate Gaussian profile within 1% error. Though Bessel function based discrete kernels have been proposed earlier, they involve computations on real numbers and hence increasing the computational complexity. However, the integer sequence based Gaussian approximation, discussed in this paper, offer a low cost alternative to the one using Bessel functions. � Springer International Publishing Switzerland 2014.en_US
dc.titleGaussian approximation using integer sequencesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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