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Record W2014058093 · doi:10.1198/000313006x89947

The PDF of a Function of a Random Variable

2006· article· en· W2014058093 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

VenueThe American Statistician · 2006
Typearticle
Languageen
FieldComputer Science
TopicEvolutionary Algorithms and Applications
Canadian institutionsMcGill UniversityNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsProbability density functionRandom variableMathematicsProbability mass functionVariable (mathematics)Inverse functionFunction (biology)InverseAnalogyApplied mathematicsStatistical physicsStatisticsMathematical analysisGeometryPhysics

Abstract

fetched live from OpenAlex

In many instances, the probability density function (pdf) of a function of a random variable is obtained from the pdf of the random variable, the inverse function and the derivative of this inverse. The formula tends to be memorized rather than fully understood. This article describes how we teach the structure of the new pdf by (a) treating the problem as a change in scale, rather than a “change in variable”; (b) appealing to the concept of “conservation of probabilities”; (c) using the physical analogy of “pouring” probability mass from one set of “containers” to another; and (d) dealing fully with linear transformations before considering nonlinear ones.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.222
Teacher spread0.217 · 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