Error Estimations For Indirect Measurements: Randomized Vs. Deterministic Algorithms For "Black-Box" Programs (2000)
| Venue: | Handbook on Randomized Computing, Kluwer, 2001 |
| Citations: | 27 - 13 self |
BibTeX
@INPROCEEDINGS{Kreinovich00errorestimations,
author = {Vladik Kreinovich and Raúl Trejo and Ra Ul Trejo},
title = {Error Estimations For Indirect Measurements: Randomized Vs. Deterministic Algorithms For "Black-Box" Programs},
booktitle = {Handbook on Randomized Computing, Kluwer, 2001},
year = {2000},
pages = {673--729},
publisher = {Kluwer}
}
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Abstract
In many real-life situations, it is very difficult or even impossible to directly measure the quantity y in which we are interested: e.g., we cannot directly measure a distance to a distant galaxy or the amount of oil in a given well. Since we cannot measure such quantities directly, we can measure them indirectly: by first measuring some relating quantities x1 ; : : : ; xn , and then by using the known relation between x i and y to reconstruct the value of the desired quantity y. In practice, it is often very important to estimate the error of the resulting indirect measurement. In this paper, we describe and compare different deterministic and randomized algorithms for solving this problem in the situation when a program for transforming the estimates e x1 ; : : : ; e xn for x i into an estimate for y is only available as a black box (with no source code at hand). We consider this problem in two settings: statistical, when measurements errors \Deltax i = e x i \Gamma x i are inde...
