Similarity-Based Multiple Model Adaptive Estimation
Bibliographic record
Abstract
Multiple model adaptive estimation (MMAE) methods are frequently used to overcome the parametric uncertainty of the system's model. Most MMAE methods approximate the state posteriori (posterior probability) by a weighted arithmetic average of model posteriories using a Bayesian weighting scheme. Despite its effectiveness, arguably arithmetic averaging is not the most proper type of averaging for probability densities. Besides, the exploited Bayesian weighting scheme eventually reduces the MMAE to the single best candidate model, which is problematic in many scenarios. Motivated by such shortcomings, this paper proposes a similarity-based approach for MMAE which enhances the estimation accuracy by generalizing the model averaging scheme and providing realistic weights for each model. The proposed approach provides a posteriori which on average is closest to all posteriories and assigns weights to each model based on their similarity to the true model. The choice of similarity measure leads to various schemes. The simulation results confirm the superiority of the proposed MMAE methods as compared to the conventional method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".