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Record W2162567282 · doi:10.1109/isit.2006.261683

Analyticity of Entropy Rate in Families of Hidden Markov Chains (II)

2006· article· en· W2162567282 on OpenAlexaff
Guangyue Han, Brian Marcus

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicMarkov Chains and Monte Carlo Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMarkov chainEntropy (arrow of time)Radius of convergenceMathematicsTaylor seriesMaximum-entropy Markov modelApplied mathematicsMarkov processRate of convergenceStatistical physicsHidden Markov modelEntropy rateConvergence (economics)Markov propertyPure mathematicsMarkov modelBinary entropy functionPrinciple of maximum entropyComputer scienceMathematical analysisPhysicsStatisticsArtificial intelligenceThermodynamics

Abstract

fetched live from OpenAlex

We give relaxed sufficient conditions (compared to D. Blackwell (1957)) for analyticity of the entropy rate of a hidden Markov chain. Several special cases of the relaxed conditions are discussed. A general principle to calculate the domain of analyticity is stated. An example is given to estimate the radius of convergence for the entropy rate. Finally, we prove a "stabilizing" property of "black hole" case, which suggests that one can explicitly compute the derivatives and obtain an explicit Taylor series in certain cases, generalizing the results in O. Zuk et al. (2004)

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.311
Teacher spread0.280 · 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 teacher head, 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

Citations15
Published2006
Admission routes1
Has abstractyes

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