Connectable and Independent Junction Tree-Based Compilation Technique of Object-Oriented Bayesian Networks
Bibliographic record
Abstract
Object-oriented Bayesian network (OOBN) is a method for building compositional and hierarchical Bayesian network (BN) models that promote reuse and simple maintenance. Reasoning with both BNs and OOBNs entails the computational job of inference, the computation of new posterior probability distributions based on a set of evidence. A widely used inference strategy in conventional BN is to compile the BN into a junction tree (JT) before conducting standard inference. In the case of OOBN, it is first flattened into the underlying BN before performing the JT-based compilation. However, large OOBNs flatten to complex and larger BNs can be computationally intensive to compile into JTs due to the complexity of compilation being exponential to the size of BNs. To cope with these performance issues, techniques like Incremental Compilation (IC) avoid reconstructing JT from scratch after each modification of a BN. However, none of the existing works were able to reduce the computational complexity of compilation. Hence, in this paper, we propose a new compilation algorithm that compiles the OOBN without flattening it and re-using the existing JTs of embedded components of the OOBN. Evaluation results show that our proposed algorithm effectively reduces the computation time for JT construction of OOBN.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".