Bayesian inference: an approach to statistical inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The original Bayes used an analogy involving an invariant prior and a statistical model and argued that the resulting combination of prior with likelihood provided a probability description of an unknown parameter value in an application; the combination in particular contexts with invariance can currently be called a confidence distribution and is subject to some restrictions when used to construct confidence intervals and regions. The procedure of using a prior with likelihood has now, however, been widely generalized with invariance being extended to less restrictive criteria such as non‐informative, reference, and more. Other generalizations are to allow the prior to represent various forms of background information that is available or elicited from those familiar with the statistical context; these can reasonably be called subjective priors. Still further generalizations address an anomaly where marginalization with a vector parameter gives results that contradict the term probability; these are Dawid, Stone, Zidek marginalization paradoxes; various priors for this are called targeted priors. A special case where the prior describes a random source for the parameter value is however just probability analysis but is frequently treated as a Bayes procedure. We survey the argument in support of probability characteristics and outline various generalizations of the original Bayes proposal. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it