<title>Spline filter for nonlinear/non-Gaussian Bayesian tracking</title>
Bibliographic record
Abstract
This paper presents a method for the realization of nonlinear/non-Gaussian Bayesian filtering based on spline interpolation. Sequential Monte Carlo (SMC) approaches are widely used in nonlinear/non-Gaussian Bayesian filtering in which the densities are approximated by taking discrete set of points in the state space. In contrast to the SMC methods, the proposed approach uses spline polynomial interpolation to approximate the probability densities as well as the likelihood functions. A good representation of the probability densities is an essential issue in the success of the filtering algorithm, especially in nonlinear filtering, since the probability densities in nonlinear filtering could be multi-modal. An advantage of the proposed approach is that it retains the accurate density information and thus a target probability at any region in the state space can easily be obtained by evaluating the integral of the polynomial. Further, the probability densities are represented with polynomials of fixed order and any further processing on probability densities could be performed with less computation. This paper uses the B-spline interpolation in order to maintain the positivity of probability density functions and likelihood functions. Simulation results are presented to compare the performance and computational cost of the proposed algorithm with an SMC 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".