Parameter estimation of Markov‐switching Hammerstein systems using the variational Bayesian approach
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Bibliographic record
Abstract
Parameter estimation of Markov‐switching Hammerstein systems is presented in this study. The random switching mode is described by a hidden Markov model. An input non‐linear controlled autoregressive Hammerstein model is applied to describe the dynamic process of each local model. By applying the decomposition technique to the non‐linear mapping of the input signal, each local model is written as a linear‐in‐parameter form. The variational Bayesian approach is applied to identify the switched Markov Hammerstein models. Both the discrete‐valued switching mode state and the unknown parameters in each local model are estimated. Moreover, instead of the point estimation, posterior distributions of unknown parameters for each local model are obtained. Numerical simulation examples and a hybrid tank experiment are presented to verify the effectiveness of the proposed approach.
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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 | 0.001 | 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 | 0.000 | 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