Critical remarks on belief updating in Bayesian networks
Why this work is in the frame
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Bibliographic record
Abstract
There exists a number of problems in the traditional method for belief updating. First, it is generally believed that the junction tree propagation (JTP) method cannot compute p(X|e) when X is not contained in a node of the junction tree. Secondly, the local propagation procedure has to be applied whenever new evidence is observed. Many researchers have attempted to solve the first problem. Contrary to common belief, in this paper we show that one can in fact easily compute p(X\e) by the standard JTP method for any X. We also show that it is not necessary to repeat the local propagation procedure for processing new evidence. More importantly, perhaps, we suggest a more efficient method for belief updating. Our method requires to compute the marginals of the individual nodes in the junction tree only once.
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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 | 0.001 |
| 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