Efficient reversible-jump Markov-chain Monte Carlo sampling in trans-dimensional Bayesian geoacoustic inversion
Bibliographic record
Abstract
This paper considers efficient computational approaches to estimate the posterior probability density (PPD) of seabed geoacoustic profiles in the Bayesian inversion of ocean acoustic data, a numerically intensive problem. Trans-dimensional (trans-D) inversion is applied, which samples probabilistically over an unknown number of seabed layers as well as the layer geoacoustic properties and parameters of the error model (variances and autoregressive coefficients). Sampling is based on the reversible-jump Markov-chain Monte Carlo algorithm, the efficiency of which depends strongly on the formulation of the proposal density by which new candidate models are generated for probabilistic acceptance/rejection. A highly efficient proposal density is presented which combines principal-component (PC) reparameterization with parallel tempering. PC reparameterization applies an adaptive linearized approximation to the PPD as the proposal density, which provides effective directions and length scales for model perturbations in high-dimensional parameter spaces. Parallel tempering considers a series of interacting Markov chains with successively relaxed likelihood functions, which greatly improves the sampling of multi-modal parameter spaces and trans-D transitions. These approaches are combined by computing different PC reparameterizations for each Markov chain in the parallel tempering formulation. Inversion results are presented as marginal probability profiles for geoacoustic properties, marginalized over the number of layers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".