Testing and Improving Local Adaptive Importance Sampling in LFJ Local-JT in Multiply Sectioned Bayesian Networks
Bibliographic record
Abstract
Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems.The exact inference is costly and difficult to be applied in the context of MSBNs as the size of problem domain becomes larger and complex.So the approximate techniques are used as an alternative in such cases.Recently, for reasoning in MSBNs, LJFbased Local Adaptive Importance Sampler (LLAIS) has been developed for approximate reasoning in MSBNs.However, the prototype of LLAIS is tested only on Alarm Network (37 nodes).But further testing on larger networks has not been reported yet, so the scalability and reliability of algorithm remains questionable.Hence, we tested LLAIS on three large networks (treated as local JTs) namely Hailfinder (56 nodes), Win95pts (76 nodes) and PathFinder(109 nodes).From the experiments done, it is seen that LLAIS without parameters tuned shows good convergence for Hailfinder and Win95pts but not for Pathfinder network.Further when these parameters are tuned the algorithm shows considerable improvement in its accuracy and convergence for all the three networks tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".