An adaptive nonparametric particle filter for state estimation
Why this work is in the frame
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Bibliographic record
Abstract
Particle filter is one of the most widely applied stochastic sampling tools for state estimation problems in practice. However, the proposal distribution in the traditional particle filter is the transition probability based on state equation, which would heavily affect estimation performance in that the samples are blindly drawn without considering the current observation information. Additionally, the fixed particle number in the typical particle filter would lead to wasteful computation, especially when the posterior distribution greatly varies over time. In this paper, an advanced adaptive nonparametric particle filter is proposed by incorporating gaussian process based proposal distribution into KLD-Sampling particle filter framework so that the high-qualified particles with adaptively KLD based quantity are drawn from the learned proposal with observation information at each time step to improve the approximation accuracy and efficiency. Our state estimation experiments on univariate nonstationary growth model and two-link robot arm show that the adaptive nonparametric particle filter outperforms the existing approaches with smaller size of particles.
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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 | 0.000 | 0.001 |
| 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