Simple Propagation with Arc-Reversal in Bayesian Networks
Bibliographic record
Abstract
Simple Propagation is a recently introduced algorithm for inference in discrete Bayesian networks using message passing in a junction tree. Simple Propagation is similar to Lazy Propagation, but uses the simple one in, one out-principle when computing messages between cliques of the junction tree instead of using a more in-depth graphical analysis of the set of potentials. In this paper, we describe how to apply Arc-Reversal (AR) as the marginalization algorithm during message passing in Simple Propagation. We consider both discrete and hybrid Bayesian networks, where the continuous variables are assumed to be Conditional Linear Gaussian (CLG). The use of AR eliminates the need for complex matrix operations in case of CLG networks, while offering opportunities to exploit additional independence and irrelevance properties in both cases when compared to Variable Elimination (VE). The performance of Simple Propagation with AR has been evaluated on a set of real-world Bayesian networks with discrete variables and hybrid Bayesian networks constructed by randomly replacing discrete variables with continuous variables under the CLG constraints. The performance of Simple Propagation with AR is compared with the performance of Lazy Propagation with AR. The results of the experimental performance analysis of Simple Propagation with AR are encouraring
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".