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Record W2070925289 · doi:10.3934/amc.2014.8.119

Nearest-neighbor entropy estimators with weak metrics

2014· preprint· en· W2070925289 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Mathematics of Communications · 2014
Typepreprint
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEstimatorMathematicsEntropy estimationEntropy (arrow of time)Upper and lower boundsMinimax estimatorErgodic theoryk-nearest neighbors algorithmBounded functionApplied mathematicsMaximum entropy probability distributionMinimum-variance unbiased estimatorNonparametric statisticsStatisticsPrinciple of maximum entropyComputer scienceArtificial intelligenceMathematical analysisPhysics

Abstract

fetched live from OpenAlex

A problem of improving the accuracy of nonparametric entropy estimationfor a stationary ergodic process is considered. New weak metrics are introduced and relations between metrics,measures, and entropy are discussed. A new nonparametric entropy estimator is constructed based on weak metricsand has a parameter with which the estimator is optimized to reduce its bias.It is shown that estimator's variance is upper-bounded by a nearly optimal Cramér-Rao lower bound.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0060.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.332
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it