Clustering Time Series with Nonlinear Dynamics: A Bayesian\n Non-Parametric and Particle-Based Approach
Bibliographic record
Abstract
We propose a general statistical framework for clustering multiple time\nseries that exhibit nonlinear dynamics into an a-priori-unknown number of\nsub-groups. Our motivation comes from neuroscience, where an important problem\nis to identify, within a large assembly of neurons, subsets that respond\nsimilarly to a stimulus or contingency. Upon modeling the multiple time series\nas the output of a Dirichlet process mixture of nonlinear state-space models,\nwe derive a Metropolis-within-Gibbs algorithm for full Bayesian inference that\nalternates between sampling cluster assignments and sampling parameter values\nthat form the basis of the clustering. The Metropolis step employs recent\ninnovations in particle-based methods. We apply the framework to clustering\ntime series acquired from the prefrontal cortex of mice in an experiment\ndesigned to characterize the neural underpinnings of fear.\n
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".