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Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far.

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  • Reservoir sampling (en)
  • Reservoir sampling (ru)
  • 水塘抽樣 (zh)
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  • Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. (en)
  • Reservoir sampling («резервуарная выборка», нет устоявшегося русского перевода) представляет собой семейство вероятностных алгоритмов произвольного выбора образца, состоящего из k элементов из списка S, содержащего n элементов, где n — это либо очень большое, либо неизвестное число. Обычно, n достаточно велико, чтобы весь список не уместился в основной памяти. (ru)
  • 水塘抽樣（英語：Reservoir sampling）是一系列的隨機算法，其目的在於從包含n個項目的集合S中選取k個樣本，其中n為一很大或未知的數量，尤其適用於不能把所有n個項目都存放到内存的情況。最常見例子為在其論文中所提及的算法R。 參照Dictionary of Algorithms and Data Structures所載的算法，包含以下步驟（假設数组S以0開始標示）： 從S中抽取首k項放入「水塘」中對於每一個S[j]項（j ≥ k）： 隨機產生一個範圍從0到j的整數r 若 r < k 則把水塘中的第r項換成S[j]項 (zh)
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  • December 2020 (en)
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  • The statement is correct, I think; however the source cited mentions the exponential distribution but doesn't seem to mention any relationship with the Efraimidis and Spirakis algorithm, or anything about stability. (en)
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  • Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. (en)
  • Reservoir sampling («резервуарная выборка», нет устоявшегося русского перевода) представляет собой семейство вероятностных алгоритмов произвольного выбора образца, состоящего из k элементов из списка S, содержащего n элементов, где n — это либо очень большое, либо неизвестное число. Обычно, n достаточно велико, чтобы весь список не уместился в основной памяти. (ru)
  • 水塘抽樣（英語：Reservoir sampling）是一系列的隨機算法，其目的在於從包含n個項目的集合S中選取k個樣本，其中n為一很大或未知的數量，尤其適用於不能把所有n個項目都存放到内存的情況。最常見例子為在其論文中所提及的算法R。 參照Dictionary of Algorithms and Data Structures所載的算法，包含以下步驟（假設数组S以0開始標示）： 從S中抽取首k項放入「水塘」中對於每一個S[j]項（j ≥ k）： 隨機產生一個範圍從0到j的整數r 若 r < k 則把水塘中的第r項換成S[j]項 (zh)
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