Approximate MMSE Estimator for Linear Dynamic Systems with Gaussian\n Mixture Noise
Bibliographic record
Abstract
In this work we propose an approximate Minimum Mean-Square Error (MMSE)\nfilter for linear dynamic systems with Gaussian Mixture noise. The proposed\nestimator tracks each component of the Gaussian Mixture (GM) posterior with an\nindividual filter and minimizes the trace of the covariance matrix of the bank\nof filters, as opposed to minimizing the MSE of individual filters in the\ncommonly used Gaussian sum filter (GSF). Hence, the spread of means in the\nproposed method is smaller than that of GSF which makes it more robust to\nremoving components. Consequently, lower complexity reduction schemes can be\nused with the proposed filter without losing estimation accuracy and precision.\nThis is supported through simulations on synthetic data as well as experimental\ndata related to an indoor localization system. Additionally, we show that in\ntwo limit cases the state estimation provided by our proposed method converges\nto that of GSF, and we provide simulation results supporting this in other\ncases.\n
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".