A simple and efficient eM-algorithm for one-shot device data analysis
Bibliographic record
Abstract
In this paper, we propose a simple and efficient EM-algorithm for estimating the model parameters based on one-shot device data. Traditionally, in the classical Expectation Maximization algorithm (EM-algorithm), unobserved failure times are regarded as the missing information and then imputed. In contrast, we consider here the counts of failures occurring between two successive inspection times to be missing. We provide detailed procedures, assuming that the lifetimes of one-shot devices follow the exponential and Weibull distributions, respectively. A Monte Carlo simulation study reveals that this simple approach markedly increases the convergence speed, a perennial challenge when using the EM-algorithm. The new method consistently finds the maximum likelihood estimates, unlike the traditional method which fails sometimes because some expectations do not exist based on the parameter estimates obtained from the previous M-step. Finally, an example is provided for illustrative purpose.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".