StreamSampling.jl implements efficient one-pass sampling algorithms for data streams in Julia with constant memory footprint and performance gains over traditional methods.
An asymptotically optimal, online algorithm for weighted random sampling with replacement
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abstract
This paper presents a novel algorithm solving the classic problem of generating a random sample of size s from population of size n with non-uniform probabilities. The sampling is done with replacement. The algorithm requires constant additional memory, and works in O(n) time (even when s >> n, in which case the algorithm produces a list containing, for every population member, the number of times it has been selected for sample). The algorithm works online, and as such is well-suited to processing streams. In addition, a novel method of mass-sampling from any discrete distribution using the algorithm is presented.
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cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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StreamSampling.jl: Efficient Sampling from Data Streams in Julia
StreamSampling.jl implements efficient one-pass sampling algorithms for data streams in Julia with constant memory footprint and performance gains over traditional methods.