State Estimation for General Class of Dynamical Systems: An Extension to Particle Filters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many physical systems are nonlinear and non-Gaussian in their state-space models. Particle Filter (PF) is a sequential Monte Carlo method that uses sets of sample scenarios, i.e. "particles" to represent probability densities, and it can be applied for state estimation in nonlinear/non-Gaussian state-spaces models. Conventional variants of PF do not assume any noise for the system input, while the corresponding measurement models disregard the system input as an argument. In reality, physical systems receive inputs contaminated with the measurement noise. In this work, a generalized particle filter algorithm is developed that handles the noisy input of the state-space model in a probabilistic framework. Three advanced variants of PF are then developed to improve the filtering accuracy. Performance of the developed filters are then verified with simulation of univariate and bivariate non-stationary growth models as benchmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 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