Abstract
Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic density estimation. One of its most important disadvantages is computational complexity of calculations needed, especially for data-based bandwidth selection and adaptation of bandwidth coefficient. The article presents parallel methods which can significantly improve calculation time. Results of using reference implementation based on Message Passing Interface standard in multicomputer environment are included as well as a discussion on effectiveness of parallelization.
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Łukasik, S. (2007). Parallel Computing of Kernel Density Estimates with MPI. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72588-6_120
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DOI: https://doi.org/10.1007/978-3-540-72588-6_120
Publisher Name: Springer, Berlin, Heidelberg
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