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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)