Trans-dimensional strategies for geoacoustic posterior probability estimation
Bibliographic record
Abstract
Choosing appropriate model parametrizations is a fundamental aspect of Bayesian parameter inference. Trans-dimensional inference is based on a hierarchical Bayesian model, where the number of unknowns in the problem is itself unknown. The methodology extends the state-space to the union of subspaces of a group of models of interest. This allows the data to estimate the support for the parametrizations under consideration while accounting for the limited knowledge about the parametrization in the posterior probability density estimate. Algorithms, such as reversible-jump Markov chain Monte Carlo, can be used to sample from the trans-dimensional posterior by proposing and accepting/rejecting dimensional transitions (jumps) according to a generalized Metropolis-Hastings criterion. Several challenges exist for the efficient application of trans-dimensional methods to geoacoustic inference including defining proposal distributions for efficient transitions between dimensions and within dimensions, and specifying (or quantifying) data errors distributions (or statistics). Example1109; of applying the trans-dimensional approach are demonstrated for several geoacoustic inverse problems, using trans-dimensional partition modeling and hierarchical data-error models.
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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".