Model selection using Bayesian samples: An introduction to the deviance information criterion
Bibliographic record
Abstract
This paper presents the deviance information criterion (DIC) as a metric for model selection based on Bayesian sampling approaches, with examples from seabed geoacoustic and/or scattering inversion. The DIC uses all samples of a distribution to approximate Bayesian evidence, unlike more common measures such as the Bayesian information criterion, which only use point estimates. The DIC uses distribution samples to approximate Bayesian evidence, unlike more common measures such as the Bayesian information criterion based on point estimates. Hence the DIC is more appropriate for non-linear Bayesian inversions utilizing posterior sampling. Two examples are considered: determining the dominant seabed scattering mechanism (interface and/or volume scattering), and choosing between seabed profile parameterizations based on smooth gradients (polynomial splines) or discontinuous homogeneous layers. In both cases, the DIC is applied to trans-dimensional inversions of simulated and measured data, utilizing reversible jump Markov chain Monte Carlo sampling. For the first case, the DIC is found to correctly select the true scattering mechanism for simulations, and its choice for the measured data inversion is consistent with sediment cores extracted at the experimental site. For the second case, the DIC selects the polynomial spline parameterization for soft seabeds with smooth gradients. [Work supported by ONR.]
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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 | 0.065 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".