Error Propagation in Gossip-Based Distributed Particle Filters
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the impact of the gossip procedure on distributed particle filters that employ averaging to estimate the global likelihood function. We consider a model where a gossip-driven algorithm leads to the use of a slightly distorted version of the likelihood function, in lieu of its true value. Under standard regularity conditions, and a mild assumption on the true likelihood function, we derive a time-uniform bound on the weaksense Lp error of the filter. Furthermore, we present an associated exponential inequality for the large deviations of the filter. These bounds capture the combined effects of sampling and consensusbased approximation. The results allow us to evaluate the impact of such approximations on the overall performance of the distributed particle filter, and analyze its stability. Finally, through numerical experiments, we demonstrate the practical implications of these results and explore the relationship of the performance of the filter with these theoretical error bounds.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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