Publication Date




Short version published in Proceedings of the Workshop on State-of-the-Art in Scientific Computing PARA'04, Lyngby, Denmark, June 20-23, 2004, Vol. 1, pp. 123-129; extended version published in Jack Dongarra, Kaj Madsen, and Jerzy Wasniewski (eds.), PARA'04 Workshop on State-of-the-Art in Scientific Computing, Springer Lecture Notes in Computer Science, 2006, Vol. 3732, pp. 189-196.


It is known that in general, statistical analysis of interval data is an NP-hard problem: even computing the variance of interval data is, in general, NP-hard. Until now, only one case was known for which a feasible algorithm can compute the variance of interval data: the case when all the measurements are accurate enough -- so that even after the measurement, we can distinguish between different measured values Xi. In this paper, we describe several new cases in which feasible algorithms are possible -- e.g., the case when all the measurements are done by using the same (not necessarily very accurate) measurement instrument -- or at least a limited number of different measuring instruments.

tr04-04a.pdf (139 kB)
Updated version: UTEP-CS-04-04a

tr04-04.pdf (129 kB)
Original file: UTEP-CS-04-04