Macroscopic Models of Clique Tree Growth for Bayesian Networks
Bibliographic record
Abstract
in clique tree clustering inference consists of propagation in a clique tree compiled from a bayesian network in this paper we develop an analytical approach to characterizing clique tree growth as a function of increasing bayesian network connectedness specifically i the expected number of moral edges in their moral graphs or ii the ratio of the number of non root nodes to the number of root nodes in experiments we systematically increase the connectivity of bipartite bayesian networks and find that clique tree size growth is well approximated by gompertz growth curves this research improves the understanding of the scaling behavior of clique tree clustering provides a foundation for benchmarking and developing improved bn inference algorithms and presents an aid for analytical trade off studies of tree clustering using growth curves reference o j mengshoel macroscopic models of clique tree growth for bayesian networks in proc of the 22nd national conference on artificial intelligence aaai 07 july 2007 vancouver canada pp 1256 1262 bibtex reference inproceedings mengshoel07macroscopic author mengshoel o j title macroscopic models of clique tree growth for bayesian networks year 2007 booktitle proceedings of the twenty second national conference on artificial intelligence aaai 07 pages 1256 1262 address vancouver british columbia
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".