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Record W2339007906 · doi:10.1109/tit.2016.2553147

Arbitrarily Tight Bounds on Differential Entropy of Gaussian Mixtures

2016· article· en· W2339007906 on OpenAlexaff
Kamyar Moshksar, Amir K. Khandani

Bibliographic record

VenueIEEE Transactions on Information Theory · 2016
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDifferential entropyGaussianUpper and lower boundsMathematicsEntropy (arrow of time)Sequence (biology)Information theoryGaussian processAmplitudeAlgorithmStatistical physicsApplied mathematicsMathematical analysisPrinciple of maximum entropyMaximum entropy probability distributionPhysicsStatisticsQuantum mechanics

Abstract

fetched live from OpenAlex

A sequence of lower and upper bounds is derived on the differential entropy of a Gaussian mixture where the Gaussian components only differ in mean values. As the sequence index increases, the computational complexity of the bounds increases; however, the gap between the lower and upper bounds becomes vanishingly small. We address the applications of these bounds in several communication scenarios where the transmitters utilize Pulse Amplitude Modulation (PAM) constellations to transmit data.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.046
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0020.008
Scholarly communication0.0040.009
Open science0.0030.010
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2016
Admission routes1
Has abstractyes

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