Probabilistic Monitoring of Sensors in State-Space With Variational Bayesian Inference
Why this work is in the frame
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Bibliographic record
Abstract
Measurements quality is important for process systems engineering. In this paper, an estimation scheme is proposed in the state-space form to monitor the degree of accuracy of measurements within a predefined horizon. Under the assumption that all the sensors are uncorrelated with each other, the distribution of measurement noise covariance as well as the distribution of state vector are estimated simultaneously. The key technique is to approximate the true posterior distribution by two independent proposal distributions using the variational Bayesian inference. It is shown that the proposed algorithm provides not only a complete picture of the working status of each sensor, but also satisfied estimates of the hidden states in the presence of faulty signals. Numerical examples with a moving target tracking model and a quadrate water tank experiment are conducted to demonstrate that the proposed method exhibits better performance than the existing methods, and even a small fluctuation of sensors can be accurately captured by the proposed algorithm.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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