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Record W2128598176 · doi:10.1111/1467-9469.00250

Markov Chains and De‐initializing Processes

2001· article· en· W2128598176 on OpenAlexaff
Gareth O. Roberts, Jeffrey S. Rosenthal

Bibliographic record

VenueScandinavian Journal of Statistics · 2001
Typearticle
Languageen
FieldMathematics
TopicMarkov Chains and Monte Carlo Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMarkov chainInitializationMarkov chain Monte CarloMarkov chain mixing timeMathematicsConvergence (economics)Variable-order Markov modelExamples of Markov chainsMarkov processAdditive Markov chainMarkov modelMarkov propertyApplied mathematicsMarkov kernelMonte Carlo methodMathematical optimizationComputer scienceStatisticsEconomics

Abstract

fetched live from OpenAlex

We define a notion of de‐initializing Markov chains. We prove that to analyse convergence of Markov chains to stationarity, it suffices to analyse convergence of a de‐initializing chain. Applications are given to Markov chain Monte Carlo algorithms and to convergence diagnostics.

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)0.004
metaresearch head score (Gemma)0.014
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.062
GPT teacher head0.359
Teacher spread0.297 · 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

Citations74
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueScandinavian Journal of StatisticsSame topicMarkov Chains and Monte Carlo MethodsFrench-language works237,207