A variational Bayesian approach to identification of switched ARX models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the identification of switched Auto-Regressive eXogenous (SARX) models, the number of local models is often assumed to be known a priori. However, in many industrial applications the prior process knowledge or the available information about the plant operation might not be sufficient to determine the number of local models. In such cases, the optimal number of local models needs to be inferred from collected operational data. The switching mechanism of the process is also often unknown. Therefore, classical SARX identification methods assuming a piecewise affine system fail to accurately identify randomly switched models. Furthermore, classical identification methods result in single-point estimates of unknown parameters, thereby ignoring the parameter uncertainty. The main objective of this work is to formulate and solve the problem of SARX model identification under the variational Bayesian framework through which the aforementioned challenging issues can be addressed. As a full Bayesian system identification approach, the proposed method not only provides a posterior distribution over model parameters to reveal the level of uncertainty of the estimated values, but also determines the optimal number of local models automatically. Since the identification pair identity at each sampling instant can be inferred from the data set, the switching mechanism will not influence the identification results. The effectiveness of the proposed Bayesian approach is demonstrated through a simulation case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it