Interaction Analysis of Multivariate Control Systems Under Bayesian Framework
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
Detection and quantification of interactions between the loops of a multivariable system are of interest for different purposes, such as control system design, optimization, fault diagnosis, and performance assessment. This paper proposes a new method for interaction analysis based on decomposing the estimated transfer function between variables in the form of impulse response coefficients. The method not only provides an estimation of the direct (feedback and interaction free) transfer function between the variables, but also provides a measure of strength of all the indirect paths connecting variables together individually. The advantage of the method is that it provides a complete picture of the different paths through which variables can influence each other along with an estimation of the energy transferred through each path independently. The analysis is performed by estimating structural vector autoregressive models under Bayesian framework. Bayesian approach provides certain advantages in terms of dealing with high dimensional variables and overparameterization problem. An appropriate design of the prior probability for the model parameters also better ensures convergence to a physically interpretable model. A procedure to design the prior distribution for the model parameters is presented in this paper.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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