Simple Analysis of Priority Sampling
Majid Daliri, Juliana Freire, Christopher Musco, A\'ecio Santos,, Haoxiang Zhang

TL;DR
This paper provides a simplified and concise proof of a tight variance bound for the priority sampling method, resolving a longstanding conjecture in the field.
Contribution
It introduces a shorter, more accessible proof of the variance bound for priority sampling, improving understanding and potentially enabling further research.
Findings
Proved a tight upper bound on the variance of priority sampling
Simplified the proof significantly compared to previous work
Resolved a conjecture posed by Duffield, Lund, and Thorup
Abstract
We prove a tight upper bound on the variance of the priority sampling method (aka sequential Poisson sampling). Our proof is significantly shorter and simpler than the original proof given by Mario Szegedy at STOC 2006, which resolved a conjecture by Duffield, Lund, and Thorup.
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Taxonomy
TopicsMachine Learning and Algorithms · Sparse and Compressive Sensing Techniques · Complexity and Algorithms in Graphs
