Join Bayes Nets: A new type of Bayes net for relational data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning has developed a number of new statistical models for such data. Instead of introducing a new model class, we propose using a standard model class—Bayes nets—in a new way: Join Bayes nets contain nodes that correspond to the descriptive attributes of the database tables, plus Boolean relationship nodes that indicate the presence of a link. Join Bayes nets are class-level models whose random variables describe attributes of generic individuals (e.g., age(P) rather than age(Jack) where P stands for a randomly selected person). As Join Bayes nets are just a special type of Bayes net, their semantics is standard (edges denote direct associations, d-separation implies probabilistic independence etc.), and Bayes net inference algorithms can be used “as is ” to answer probabilistic queries involving relations. We present a dynamic programming algorithm for estimating the parameters of a Join Bayes net and discuss how Join Bayes Nets model various well-known statistical-relational phenomena like autocorrelation and aggregation. 1
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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 | 0.000 | 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