New bounds on the probability of a finite union of events
Bibliographic record
Abstract
The classes of all lower/upper bounds on the probability of a finite union of events which are expressed only in terms of the individual event probabilities and the sums of the pairwise event probabilities are considered. The optimal lower and upper bounds in each class are given numerically by solving a linear programming (LP) problem. Furthermore, a suboptimal analytical lower bound is established by solving a relaxed LP problem, which is at least as good as an existing bound due to Kuai, et al. [1]. Note that the new lower bounds can be further improved algorithmically by optimizing them over subsets [2], [3], and can be applied to general estimation problems involving the probability of a finite union. Finally, the new lower/upper bounds are illustrated by examining the symbol and bit error rates of an uncoded communication system used in conjunction with Mary phase-shift keying (PSK) modulation over additive white Gaussian noise (AWGN) channels under maximum a posteriori (MAP) decoding.
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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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".