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Record W2562855046 · doi:10.5539/ijsp.v6n1p24

Limit Distribution of a Generalized Ornstein -- Uhlenbeck Process

2016· article· en· W2562855046 on OpenAlexvenueno aff
Andriy Yurachkivsky

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2016
Typearticle
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsRandom variableOrnstein–Uhlenbeck processLimit (mathematics)CombinatoricsDistribution (mathematics)Bounded functionStochastic processDiscrete mathematicsMathematical analysisStatistics

Abstract

fetched live from OpenAlex

Let an $\bR^d$-valued random process $\xi$ be the solution of an equation of the kind $\xi(t)=\xi(0)+\int_0^tA(u)\xi(u)\rd\iota(u)+S(t),$ where $\xi(0)$ is a random variable measurable w.\,r.\,t. some $\sigma$-algebra $\cF(0)$, $S$ is a random process with $\cF(0)$-conditionally independent increments, $\iota$ is a continuous numeral random process of locally bounded variation, and $A$ is a matrix-valued random process such that for any $t>0$ $\int_0^t\|A(s)\|\ |\rd\iota(s)|<\iy.$ Conditions guaranteing existence of the limiting, as $t\to\iy$, distribution of $\xi(t)$ are found. The characteristic function of this distribution is written explicitly.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.002
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 head0.033
GPT teacher head0.333
Teacher spread0.300 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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