In this paper, we consider the problem of heterogeneous subset sampling. Each element in a domain set has a different probability of being included in a sample, which is a subset of the domain set. Drawing a sample from a domain set of size n takes O(n) time if a Naive algorithm is employed. We propose a Hybrid algorithm that requires O(n) preprocessing time and O(n) extra space. On average, it draws a sample in time, where p * is min (p μ , 1-p μ ) and p μ denotes the mean of inclusion probabilities. In the worst case, it takes O(n) time to draw a sample. In addition to the theoretical analysis, we evaluate the performance of the Hybrid algorithm via experiments and present an application for particle-based simulations of the spread of a disease. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tsai, M. T., Wang, D. W., Liau, C. J., & Hsu, T. S. (2010). Heterogeneous subset sampling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6196 LNCS, pp. 500–509). https://doi.org/10.1007/978-3-642-14031-0_53
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