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Record W2790909449 · doi:10.1002/wics.110

Likelihood inference

2010· review· en· W2790909449 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

VenueWiley Interdisciplinary Reviews Computational Statistics · 2010
Typereview
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLikelihood functionLikelihood principleInferenceEmpirical likelihoodMaximum likelihoodRestricted maximum likelihoodMarginal likelihoodParametric statisticsQuasi-maximum likelihoodComputer scienceBayesian inferenceBayesian probabilityLikelihood-ratio testEconometricsMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

Abstract The essential role of the likelihood function in both Bayesian and non‐Bayesian inference is described. Several topics related to the extension of likelihood‐based methodology to more complex settings are reviewed, including modifications to profile likelihood, composite and pseudo‐likelihoods, quasi‐likelihood, semi‐parametric and non‐parametric likelihoods, and empirical likelihood. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Algorithms and Computational Methods > Maximum Likelihood Methods

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.120
GPT teacher head0.471
Teacher spread0.352 · 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