Affirmative sampling: theory and applications
Visualitza/Obre
10.4230/LIPIcs.AofA.2022.12
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/377535
Tipus de documentText en actes de congrés
Data publicació2022
EditorSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement 4.0 Internacional
Abstract
Affirmative Sampling is a practical and efficient novel algorithm to obtain random samples of distinct elements from a data stream. Its most salient feature is that the size S of the sample will, on expectation, grow with the (unknown) number n of distinct elements in the data stream. As any distinct element has the same probability to be sampled, and the sample size is greater when the “diversity” (the number of distinct elements) is greater, the samples that Affirmative Sampling delivers are more representative than those produced by any scheme where the sample size is fixed a priori - hence its name. Our algorithm is straightforward to implement, and several implementations already exist.
CitacióLumbroso, J.; Martinez, C. Affirmative sampling: theory and applications. A: International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms. "33rd International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms, AofA 2022: June 20-24, 2022, Philadelphia, PA, USA". Wadern: Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022, p. 12:1-12:17. ISBN 978-3-95977-230-3. DOI 10.4230/LIPIcs.AofA.2022.12.
ISBN978-3-95977-230-3
Versió de l'editorhttps://drops.dagstuhl.de/opus/frontdoor.php?source_opus=16098
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