Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data
Bibliographic record
Abstract
Bayesian Networks (BNs) are a widely utilized formalism for representing knowledge in intelligent agents on partially observable and stochastic application environments. When conditional probability tables are used in BNs to quantify strength of dependency between each variable and its parents, the space complexity is exponential on the number m of parents per variable. The time complexity of inference is also lower-bounded exponentially by m. The non-impeding noisy-AND Tree (NAT) model-based BNs can signi cantly improve both space and time complexity above, rendering both complexity measures linear on m, for a wide range of sparse BN structures. This research studies learning NAT model-based BNs from data by applying the Minimum Description Length principle and heuristic search. It advances BN structure learning with local models by focusing on inequality constraints. Practitioners can make tractable inferences using such BNs learned from data, especially when data admits high treewidth and low-density structures.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".