On Marginal Likelihood Computation in Change-Point Models
Bibliographic record
Abstract
Change-point models are useful for modeling time series subject to structural breaks. For\ninterpretation and forecasting, it is essential to estimate correctly the number of change points in\nthis class of models. In Bayesian inference, the number of change points is typically chosen by\nthe marginal likelihood criterion, computed by Chib's method. This method requires to select a\nvalue in the parameter space at which the computation is done. We explain in detail how to\nperform Bayesian inference for a change-point dynamic regression model and how to compute its\nmarginal likelihood. Motivated by our results from three empirical illustrations, a simulation\nstudy shows that Chib's method is robust with respect to the choice of the parameter value used in\nthe computations, among posterior mean, mode and quartiles. Furthermore, the performance of\nthe Bayesian information criterion, which is based on maximum likelihood estimates, in selecting\nthe correct model is comparable to that of the marginal likelihood.
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How this classification was reachedexpand
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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".