Local Importance Sampling in Multiply Sectioned Bayesian Networks
Why this work is in the frame
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Bibliographic record
Abstract
The multiply sectioned Bayesian network (MSBN) is a well-studied model for probability reasoning in a multi-agent setting. Exact inference, however, becomes difficult as the problem domain grows larger and more complex. We address this issue by integrating approximation techniques with the MSBN Linked Junction Tree Forest (LJF) framework. In particular, we investigate the application of importance sampling in an LJF local junction tree. We propose an LJF local adaptive importance sampler (LLAIS) with improved sampling convergence and effective inter-agent message calculation. Our preliminary experiments confirm that the LLAIS sampler delivers a good approximation of MSBN local posterior beliefs as well as the message calculation over LJF linkage trees.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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