The Adaptive sampling revisited

• Published in: Discrete Mathematics and Theoretical Computer Science Vol. 21 no. 3; no. Analysis of Algorithms; pp. COV1 - 27
• Main Authors: Drescher, Matthew, Louchard, Guy, Swan, Yvik
• Format: Journal Article Web Resource
• Language: English
• Published: Nancy DMTCS 01.07.2019; Maison de l'informatique et des mathematiques discretes; Discrete Mathematics & Theoretical Computer Science
• Subjects: 2010 mathematics subject classification: 68r05, 68w40; [info]computer science [cs]; Adaptive algorithms; Adaptive sampling; Algorithms; asymmetric adaptive sampling; cache; colored keys; Computer Science; Distribution functions; Hashing functions; key multiplicity; Mathematical research; Mathematics; Mathématiques; moments; periodic components; Physical, chemical, mathematical & earth Sciences; Physique, chimie, mathématiques & sciences de la terre; Random variables; Sampling (Statistics); stein method; urn model; Belgium; cache; moments; urn model; Adaptive sampling; periodic components; hashing functions; colored keys; key multiplicity; Stein method; asymmetric adaptive sampling

The problem of estimating the number n of distinct keys of a large collection of N data is well known in computer science. A classical algorithm is the adaptive sampling (AS). n can be estimated by R2 J , where R is the final bucket size and J is the final depth at the end of the process. Several ne...