A random-discretization based Monte Carlo method for numerical integration
Bibliographic record
Abstract
Tables 2.7 Variance of Importance Sampling 3.1 Comparison of IS and AIS 4.7 Performance comparison between IS and SOIR for example 4 4.2 Performance comparison between IS ancl SOIR in example b Comparison of IS, AIS and SOiR in example 7 Numerical Comparison of AIS and SOIR of Example 8, Comparison of Transformation rnethod and SOIR in Keister (1996) example 5.4 Value of for Genz test functions 5.5 Numerical Results for Three Genz Test Functions 5.1 5.2 It d.J .t UJ 71 9, and 10 Capstick and 74 -r t 76 vi List of Figures 2.7 Linear importance sampler fot g (r) with respect to the value of b 2.2 Variance of ,n as a function of b, Example 2 3.1 2-D Normal distribution contour and density graphs 3.2 Distribution of g(r) in example 4 . .3.3 Valiance of.,n as a function of b, Example 4' 4.7 sampling region of g(r) Between IS and soIR in example 4 . . . . .4.2 Distribution of g(x) of example 5 . .50 4.3 Sampling region of g@) with IS and SOIR in example 5 . . .52 b.1 Histogram of rnarginal distribution of exarnple 6 ovel initial importance sampling region 63 5.2 Histogram of rnarginal distribution of example 6 over irnproved impor- tance sampling region 64 b.3 Histograrn of marginal clistribution of example 7 over improved impor- tance samPling region 66 5.4 Histograrn of marginal distlibution of exarnple 8 over importance re- gion-part1 70 b.bHistogr-am of marginal distribution of exarnple 8 over importance re- gion -part 2 b.6 Histogram of marginal distlibution of the five-dimensional mixture beta distribution after 4th iteration by using SOIR77 v11 78 5.7 Histograrn of malginal distribution of Capstick and Keister (1996) ex- ample over importance region -part 1 .5.8 Histograrn of rnarginal distribution of Capstick and Keister (1996) ex- ampleoverimportanceregion-part2... 5.9 Histogram of marginal distribution of Genz Ploduct Peak test function over initial importance region 5.10 Histogram of marginal clistribution of Genz Product Peak test function over final irnportance region 5.11 Histogran of marginal distribution of Genz Corner Peak test function over initial importance region 5.12 Histogram of marginal distribution of Genz Cornel Peak test function over final importance region 5.13 Histogram of marginal distribution of Genz Continuous test function over initial importance region 5.14 Histogram of marginal distribution of Genz Continuous test function ovel final importance region vlll
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".