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Random Number Generation and Quasi‐<scp>M</scp>onte<scp>C</scp>arlo

2014· other· en· W4239659015 on OpenAlexaff
Pierre L’Ecuyer

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversité de MontréalComputer Research Institute of Montréal
Fundersnot available
KeywordsProbabilistic logicRandom number generationRealization (probability)Computer scienceRandom variableKey (lock)CryptographyInterval (graph theory)Theoretical computer scienceCoin flippingMonte Carlo methodAlgorithmMathematicsCombinatoricsArtificial intelligenceStatisticsComputer security

Abstract

fetched live from OpenAlex

Abstract Probability theory defines random variables and stochastic processes in terms ofprobability spaces, a purely abstract notion whose concrete and exact realization on a computer is far from obvious. (Pseudo) random number generators (RNGs) implemented on computers are actually deterministic programs, whichimitateto some extent, independent random variables uniformly distributed over the interval [0,1] (i.i.d.U[0,1], for short). RNGs are a key ingredient for Monte Carlo simulations, probabilistic algorithms, computer games, cryptography, casino machines, and so on.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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 designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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