Pith. sign in

REVIEW 1 cited by

On Generalized Sch\"urmann Entropy Estimators

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2111.11175 v1 pith:JL6KEQ6S submitted 2021-11-18 cs.IT cond-mat.stat-mechmath.ITphysics.data-an

Peter Grassberger This is my paper · ORCID
classification cs.ITcond-mat.stat-mechmath.ITphysics.data-an
keywords estimatorsentropyestimatorgeneralizationproposedarxivbayesianbelieved
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T. Schuermann, which itself is a generalization of an estimator proposed by myself in arXiv:physics/0307138. For a special set of parameters they are completely free of bias and have a finite variance, something with is widely believed to be impossible. We present also detailed numerical tests where we compare them with other recent estimators and with exact results, and point out a clash with Bayesian estimators for mutual information.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  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.

Pith tools