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題名: | On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems | 作者: | Kreinovich, V. 吳柏林 Wu, Berlin Nguyen, H.T. |
貢獻者: | 應數系 | 關鍵詞: | Algorithms; Data processing; Data reduction; Intelligent systems; Online systems; Statistical process control; Interval data; Mean; On-line data processing; Variance; Computation theory | 日期: | 八月-2007 | 上傳時間: | 13-七月-2015 | 摘要: | When we have only interval ranges [under(x, {combining low line})i, over(xi, -)] of sample values x1, ..., xn, what is the interval [under(V, {combining low line}), over(V, -)] of possible values for the variance V of these values? There are quadratic time algorithms for computing the exact lower bound V on the variance of interval data, and for computing over(V, -) under reasonable easily verifiable conditions. The problem is that in real life, we often make additional measurements. In traditional statistics, if we have a new measurement result, we can modify the value of variance in constant time. In contrast, previously known algorithms for processing interval data required that, once a new data point is added, we start from the very beginning. In this paper, we describe new algorithms for statistical processing of interval data, algorithms in which adding a data point requires only O(n) computational steps. © 2006 Elsevier Inc. All rights reserved. | 關聯: | Information Sciences, 177(16), 3228-3238 | 資料類型: | article | DOI: | http://dx.doi.org/10.1016/j.ins.2006.11.007 |
Appears in Collections: | 期刊論文 |
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