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Estimating Entropy of Distributions in Constant Space
[article]
2019
arXiv
pre-print
We consider the task of estimating the entropy of k-ary distributions from samples in the streaming model, where space is limited. Our main contribution is an algorithm that requires O(k log (1/ε)^2/ε^3) samples and a constant O(1) memory words of space and outputs a ±ε estimate of H(p). Without space limitations, the sample complexity has been established as S(k,ε)=Θ(k/εlog k+log^2 k/ε^2), which is sub-linear in the domain size k, and the current algorithms that achieve optimal sample
arXiv:1911.07976v1
fatcat:yom3mxqtsbgo7dwudqdwxqohha