Determining Bayesian evidence and decay time estimates in acoustically coupled volumes
Bibliographic record
Abstract
Sound energy decay analysis is an important element in understanding acoustical properties of coupled volumes in architectural acoustics. A Bayesian model formulation using Monte Carlo algorithms [Xiang and Goggans, J. Acoust. Soc. Am. 110 1415–1424 (2001); Jasa and Xiang, ibid. 117, 3707(A) (2005)] has been applied to estimating both the decay times and decay order required in sound energy decay analysis. The need to focus on model selection in a Bayesian model formulation of sound energy decay analysis was presented by Jasa and Xiang [J. Acoust. Soc. Am. 119, 3343(A) (2006)] along with a discussion of the limitations of the existing Monte Carlo algorithms used for this purpose. This paper will present some recent algorithms that can overcome the limitations of a Monte Carlo approach to model selection and show how these algorithms can be applied to determining both the proper model and decay time estimates for acoustically coupled rooms.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".