Bibliographic record
Abstract
Particle flow filters obtain impressive results in challenging high dimensional, non-linear sequential state estimation problems. In contrast to a particle filter, which uses importance sampling to approximate the posterior distribution of the state, the flow based algorithms solve a differential equation to migrate the particles from the prior to the posterior distribution. However, the particles after the flow are not true samples of the posterior distribution due to strong model assumptions required for the derivation of the flow and the approximations associated with the numerical solution. This affects performance adversely in many highly non-linear, non-Gaussian filtering problems. Particle Flow Particle Filters (PFPF) adapt the particle flow procedure to construct a proposal density inside the particle filter. These techniques can outperform the underlying particle flow algorithms by compensating for the approximations in the flow calculations via update of importance weights after the flow, at the cost of a negligible increase in the computational complexity. Most of the PFPF approaches have focused on using a deterministic particle flow. In this paper, we develop a PFPF algorithm using a stochastic particle flow based on Gromov's method. Numerical simulations are conducted to examine when the proposed method offers advantages compared to existing techniques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".