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Entropy and inference, revisited

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arxiv physics/0108025 v2 pith:JYM6FOLX submitted 2001-08-15 physics.data-an

Ilya Nemenman , Fariel Shafee , William Bialek This is my paper · ORCID
classification physics.data-an
keywords discretedistributionspriorsargumentdirichletdisastrousentropiesentropy
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We study properties of popular near-uniform (Dirichlet) priors for learning undersampled probability distributions on discrete nonmetric spaces and show that they lead to disastrous results. However, an Occam-style phase space argument expands the priors into their infinite mixture and resolves most of the observed problems. This leads to a surprisingly good estimator of entropies of discrete distributions.

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Cited by 1 Pith paper

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  1. To BEE or not to BEE: Estimating more than Entropy with Biased Entropy Estimators

    cs.IT 2025-01 reject novelty 5.0 of 10

    A benchmark of 18 biased entropy estimators on H, MI, and CMI concludes that Chao-Shen and Chao-Wang-Jost are fastest to converge and most accurate, though the CMI computation appears mis-specified.

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