NAT model–based compression of Bayesian network CPTs over multivalued variables
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Nonimpeding noisy‐AND tree (NAT) models offer a highly expressive approximate representation for significantly reducing the space of Bayesian networks (BNs). They also improve efficiency of BN inference significantly. To enable these advantages for general BNs, several technical advancements are made in this work to compress target BN conditional probability tables (CPTs) over multivalued variables into NAT models. We extend the semantics of NAT models beyond graded variables that causal independence models commonly adhered to and allow NAT modeling in nominal causal variables. We overcome the limitation of well‐defined pairwise causal interaction (PCI) bits and present a flexible PCI pattern extraction from target CPTs. We extend parameter estimation for binary NAT models to constrained gradient descent for compressing target CPTs over multivalued variables. We reveal challenges associated with persistent leaky causes and develop a novel framework for PCI pattern extraction when persistent leaky causes exist. The effectiveness of the CPT compression is validated experimentally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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