Compared to other approximation algorithms for the count-distinct problem the CVM Algorithm (named by [[Donald Knuth | Donald Knuth]] after the initials of Sourav Chakraborty, N. V. Vinodchandran, and Kuldeep S. Meel) uses sampling instead of hashing. The CVM Algorithm provides an unbiased estimator for the number of distinct elements in a stream<ref name=":0" />, in addition to the standard (ε-δ) guarantees<ref>{{Cite journal |last=Chakraborty |first=Sourav |last2=Vinodchandran |first2=N. V. |last3=Meel |first3=Kuldeep S. |date=2022 |title=Distinct Elements in Streams: An Algorithm for the (Text) Book |url=http://arxiv.org/abs/2301.10191 |pages=6 pages, 727571 bytes |doi=10.4230/LIPIcs.ESA.2022.34 |issn=1868-8969}}</ref> (named by [[Donald Knuth | Donald Knuth]] after the initials of Sourav Chakraborty, N. V. Vinodchandran, and Kuldeep S. Meel) uses sampling instead of hashing. The CVM Algorithm provides an unbiased estimator for the number of distinct elements in a stream<ref name=":0" />, in addition to the standard (ε-δ) guarantees. Below is the modification of CVM algorithm, proposed by Donald Knuth, that maintains a buffer of maximum size s<ref name=":0">{{cite journal |last1=Knuth |first1=Donald |date=May 2023 |title=The CVM Algorithm for Estimating Distinct Elements in Streams |url=https://cs.stanford.edu/~knuth/papers/cvm-note.pdf |journal=}}</ref>
{{nowrap|Initialize <math> p \leftarrow 1 </math>}}
