Inference of Extreme Value Distributions using Bayesian Neural Networks
Bibliographic record
Abstract
Accurate prediction of extreme weather events are crucial from a societal point of view, where the consequences of said events can have major financial and demographic impacts upon society. \nExtreme Value Theory (EVT) provides a statistical framework for the modelling of such extreme events. \nOn the other hand, Bayesian Neural Networks (BNNs) extend traditional neural networks by incorporating Bayesian inference, which provides a probabilistic approach to learning and prediction in any given regression task. \nIn this thesis, we extend the methodology of a recently introduced BNN and integrate it with EVT to be able to infer the parameters of Generalised Extreme Value (GEV) distributions. \nWe then apply our methodology to annual maximal rainfall in Eastern Canada, where we infer and interpolate GEV parameter estimates across the interpolation region. \nThe obtained results demonstrate that our approach outperforms Polynomial Regression and Inverse Distance Weighting methods in predicting extreme rainfall events.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".