Abstract
In this chapter, we show how the algorithms for estimating variance under interval and fuzzy uncertainty can be parallelized. The results of this chapter first appeared in [336].
Need for parallelization. Traditional algorithms for computing the population variance V based on the exact values x1,..., x n take linear time O(n). Algorithms for estimating variance under interval uncertainty take a larger amount of computation time – e.g., time O(n · log(n)). How can we speed up these computations?
If we have several processors, then it is desirable to perform these algorithms in parallel on several processors, and thus, speed up computations. In this chapter, we show how the algorithms for estimating variance under interval and fuzzy uncertainty can be parallelized.
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© 2012 Springer-Verlag Berlin Heidelberg
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Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G. (2012). Computing Statistics under Interval Uncertainty: Possibility of Parallelization. In: Computing Statistics under Interval and Fuzzy Uncertainty. Studies in Computational Intelligence, vol 393. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24905-1_27
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DOI: https://doi.org/10.1007/978-3-642-24905-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24904-4
Online ISBN: 978-3-642-24905-1
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