Efficient Monte Carlo random sample generation through discretization
Bibliographic record
Abstract
2 6.3 b.4 6.5 6.6Histograms of Equation (6.1) Histograms of ¡,t..o,m1 from trquation (6.2) Histograms of ¡1.. o, rn1 from Equation (6.5) Histoglams of a, B, ), rc, ancl d from Bquation (6.7) Histograms of.À¿,'i : I,2,. . ., 10 from Equation (6.8) Histograms of pf;=3, o¡{:3, and u,j(:3 from Equation 55 57 59 62 66 69 tv List of Tables 5.1 Àdean ancl Standarcl Deviation of Bctruation (5.1) 5.2 Nlean and Variance of Equation (5.2) 5.3 Ntlean, Standarcl Deviation, and N{odes of Equation (5.3) 5.4 N4ean and Standard Deviation of Equation (5 4) 5.5 Prior ancl Posterior distribution of 1( from Equation (1.4) .5.6 Parameters, Posterior \4eans ancl Stanclarcl Deviations of ¡13 and ru3 for 1{ : 3 from the Bquation (1.4) 5.7 proc.time$ in Chapter 4 a and B of Equation (6.1) À{eans, Standard Devíations, and Nlodes of p, o, m1 from Equation (6.2) Nleans and i\,fodes of a, p,7 of Bquation (6.5) Approximatecl N¡ILB, \4eans, Standard Deviations, ancl \docles of n,,0, À, a, ancl B from Equation (6.7) .Rates, N4odes, Ndeans, and Standard Deviations of Pumps À¿, i : 3B 6.1 6.2 6.3 6.4 6.5 I,2,...,10 from Equation (6.8) Prior and Posterior distribution of 1{ from Bquation (1.4) .Parameters, Posterior Means and Standard Deviations of ¡r3 ancl tu3 for 1( : 3 from the Equation (1.a) 6.8 proc.timeQinChapter6... b.b 6.7 Dr. Liqun \A/anB has been a source of encouragement to me while working in the N4aster's progran at the lJniversity of Nlanitoba.I have had the oplrortunity and great pleasure of interacting with Dr. Wang; his cloor has always been open to me.From Dr. \\¡ang's insight and experience, I have developed a keen appreciation for computational statistics.I would also like to express m)'heartfelt appreciation to Dr. Rqrpa Thulasiram in the department of Computer Science for sharing his ideas and interests for this sampling algorithm from the computational perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".