Bibliographic record
Abstract
Probabilistic reasoning is a vital part of engineering design and analysis. Inevitably it is related to decision-making – that important task of the engineer. There is a body of knowledge profound and beautiful in structure that relates probability to decision-making. This connection is emphasized throughout the book as it is the main reason for engineers to study probability. The decisions to be considered are varied in nature and are not amenable to standard formulae and recipes. We must take responsibility for our decisions and not take refuge in formulae. Engineers should eschew standard methods such as hypothesis testing and think more deeply on the nature of the problem at hand. The book is aimed at conveying this line of thinking. The search for a probabilistic ‘security blanket’ appears as futile. The only real standard is the subjective definition of probability as a ‘fair bet’ tied to the person doing the analysis and to the woman or man in the street. This is our ‘rule for life’, our beacon. The relative weights in the fair bet are our odds on and against the event under consideration. It is natural to change one's mind in the face of new information. In probabilistic inference this is done using Bayes' theorem. The use of Bayesian methods is presented in a rigorous manner. There are approximations to this line of thinking including the ‘classical’ methods of inference. It has been considered important to view these and others through a Bayesian lens.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.501 | 0.326 |
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".