Copula parameter estimation using Bayesian inference for pipe data analysis
Bibliographic record
Abstract
Water main systems are aging and becoming a growing concern for maintenance. The structural deterioration of water mains is affected by different factors, such as pipe age, pipe material, soil condition, and pipe size, among others. Various methods of modeling have been used to predict the failure of water mains. Since pipe networks are underground and obtaining data on pipe conditions is very costly, statistical modeling has been widely used for pipe condition assessment. An emerging statistical method known as copula modeling is used here for pipe data analysis. The copula method is very useful in cases where marginals belong to different families of distributions. It is also useful for generating a large number of data points when it is difficult to obtain a data set, as is the case for pipe condition assessment, and where data sets have random variables belonging to non-Gaussian family distributions. Different copula families are applied here to model the dependency between the pipe age and repair age of pipes. The paper uses a Bayesian framework to estimate the parameter values in the copula model. This approach offers an additional option for estimating copula parameters for pipe data.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".