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Record W2006677025 · doi:10.1080/03610926.2011.636168

Markov-Correlated Poisson Processes

2013· article· en· W2006677025 on OpenAlexaff
Josemar Rodrigues, N. Balakrishnan, Patrick Borges

Bibliographic record

VenueCommunication in Statistics- Theory and Methods · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMarkov chainGeneralizationMarkov renewal processBernoulli trialComputer sciencePoisson distributionFlexibility (engineering)Markov processVariable-order Markov modelMarkov modelTerm (time)Simple (philosophy)Markov chain Monte CarloMarkov propertyBernoulli's principleApplied mathematicsMathematicsStatistical physicsEconometricsStatisticsArtificial intelligenceMachine learningBayesian probabilityEngineeringPhysics

Abstract

fetched live from OpenAlex

In this article, we present a simple generalization of the Bernoulli trials model to a Markov chain with an additional parameter that measures dependence. We then formulate a Markov correlated Poisson process which, due to its flexibility, has great potential for analyzing many practical processes including those for long-term survival analysis.

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)0.005
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.258
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.027
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)0.0010.000

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 head0.075
GPT teacher head0.456
Teacher spread0.381 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2013
Admission routes1
Has abstractyes

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