Efficiency of Trans-dimensional Bayesian Inference for Geoacoustic Inversion
Bibliographic record
Abstract
This paper considers trans-dimensional (trans-D) Bayesian inference applied to a representative geoacoustic inverse problem of estimating seabed parameters from acoustic reflectivity measurements.Trans-D methods include model selection in inversion by sampling the posterior probability density (PPD) over models with differing numbers of parameters (dimensions) [1][2][3].The approach is applied here to samplie over seabed geoacoustic models with a variable number of layers [2,3], providing seabed profile estimates with uncertainties that include the uncertainty in the model parameterization.However, trans-D sampling can be computationally intensive.Sampling efficiency is largely determined by the proposal schemes applied to generate perturbed values for existing parameters and for new parameters assigned to layers added to the model.Perturbations of existing parameters are considered in a principal-component (PC) space based on an eigenvector decomposition of the unit-lag parameter covariance matrix (computed from the history of sampled models, a diminishing adaptation) [3].The relative efficiency of proposing newlayer parameters from the prior versus a Gaussian distribution focused near existing values (the common approch) is examined [3].Parallel tempering [4], which employs a sequence of interacting samplers with successively-relaxed likelihoods, is also applied to increase the acceptance rate of new layers.The relative efficiency of various proposal schemes is compared through repeated inversions with a pragmatic convergence criterion [3].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".