Suboptimal Bayesian Filters for Markov Jump Linear Systems with Unknown Noise Covariance
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Bibliographic record
Abstract
The quality of measurements plays a crucial role in industrial processes. This paper proposes a novel suboptimal filter for Markov jump linear systems (MJLSs) that deals with the challenge of unknown measurement covariance. To limit the number of feasible mode sequences, variational Bayesian (VB) inference is employed to approximate the posterior Gaussian mixture distribution. This is achieved by representing it as a product of Gaussian and categorical distribution, aiming to minimize the Kullback-Leibler (KL) divergence. The resultant recursion turns out to be a new suboptimal Bayesian estimator, adept at simultaneously estimating system states, modal state, and measurement noise covariance, all within a unified probabilistic framework. The target tracking example is presented to illustrate that the proposed method is a competitive alternative to existing suboptimal estimation methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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