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2007

Computing Population Variance and Entropy under Interval Uncertainty: Linear-Time Algorithms

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Computing Population Variance and Entropy under Interval Uncertainty: Linear-Time Algorithms
In statistical analysis of measurement results, it is often necessary to compute the range [V , V ] of the population variance V = 1 n · n i=1 (xi −E)2 (where E = 1 n · n i=1 xi) when we only know the intervals [xi − ∆i, xi + ∆i] of possible values of the xi. While V can be computed efficiently, the problem of computing V is, in general, NP-hard. In our previous paper, we showed that in a practically important case, we can use constraints techniques to compute V in time O(n · log(n)). In this paper, we provide new algorithms that compute V and, for the above case, V in linear time O(n). Similar linear-time algorithms are described for computing the range of the entropy S = − n i=1 pi · log(pi) when we only know the intervals pi = [pi , pi] of possible values of probabilities pi. 1 Computing Population Variance under Interval Uncertainty: Formulation of the Problem Once we have n measurement results x1, . . . , xn, the traditional statistical analysis starts with computin...
Gang Xiang, Martine Ceberio, Vladik Kreinovich
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2007
Where RC
Authors Gang Xiang, Martine Ceberio, Vladik Kreinovich
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