Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply local exact MaxSAT improvement to heuristic solutions | k-MAX, ETL-d, and ETL-p solutions · Treewidth-bounded BN structure learning, treewidth bounds 2, 5, and 8 | instances with extremely positive evidence (ΔBIC ≥ 10): 95%, 79%, and 78% | Helped |
| Apply BN-SLIM for 5 minutes | Heuristically computed DAGs · Benchmark DAGs | DAGs improved: 73% | Helped |
| Extend BN-SLIM runtime to 15 minutes | Heuristically computed DAGs · Benchmark DAGs | DAGs improved: 82% | Helped |
| Hand optimization from k-MAX to BN-SLIM after an initial run | k-MAX solutions · Treewidth-bounded BN structure learning | ΔBIC improvement rate | Helped |
| Apply BN-SLIM after ETL finishes | ETL-d and ETL-p solutions · Treewidth-bounded BN structure learning | instances with extremely positive evidence (ΔBIC): 84% | Helped |
| Compare BN-SLIM(k-MAX) against k-MAX in a one-hour race | k-MAX · Treewidth bound 2 | ΔBIC | Helped |
| Use a lower treewidth bound | BN-SLIM · Treewidth-bounded BN structure learning | improvement efficiency | Helped |
Closest papers by meaning
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