Automatic calculation of the sensitivity of Bayesian fisheries models to informative priors
Bibliographic record
Abstract
The derivatives of Bayes estimators, with respect to changes in hyper-parameters of the prior density, are posterior covariances. Hence, these derivatives can be readily estimated from a posterior sample and the calculation is shown to be especially straightforward for parameters having a marginal prior that is of exponential family form. Three examples are given. The first fits a Ricker curve to stockrecruit data and, for several important management parameters, examines the sensitivity of the Bayes estimates to the informative log-normal priors placed on the maximum annual reproductive rate and density-dependent compensation parameters. Using the WinBUGS software, it is demonstrated that these derivatives can easily be estimated by a minor addition to the program code. The utility of the estimated sensitivities is examined by refitting the Ricker model using a range of different priors. The second example revisits a hierarchical model that was used to perform a meta-stock assessment on several US West Coast rockfish (Sebastes spp.) stocks, and examines the sensitivity of the Bayes estimate of bulk catchability to the hyper-prior. The final example looks at an example from the literature and uses summary statistics provided therein to determine the sensitivity of model parameters to their prior means.
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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 | 0.016 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".