Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Learning Bayesian Networks with Incomplete Data by Augmentation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use exact optimistic augmentation instead of Structural EM | Bayesian network imputation · Breast Cancer | imputation accuracy: 84.38% vs. 50% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | Bayesian network imputation · Prostate Cancer | imputation accuracy: 86.36% vs. 50% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | Bayesian network imputation · ASIA | imputation accuracy: 79% vs. 43.75% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | LUCAS, spread-all-over MAR missingness · LUCAS spread all over, MAR | imputation accuracy: 70% vs. 50% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | LUCAS class-label prediction with MNAR · LUCAS classification, MNAR | accuracy: 97.5% vs. 42.5% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | LUCAS class-label prediction with MAR · LUCAS classification, MAR | accuracy: 69% vs. 70% | Hurt |
| Use approximate optimistic augmentation instead of Structural EM | SPECT imputation · SPECT, 5% missing overall | imputation accuracy: 75.22% vs. 49.27% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | SPECT imputation of value 1 · SPECT, 3% missing value = 1 | imputation accuracy: 67.83% vs. 63.48% | No clear effect |
| Use approximate optimistic augmentation instead of Structural EM | Smoking Cessation semi-supervised prediction · Smoking Cessation, 25 missing test labels | accuracy: 90% vs. 15% | Helped |
| Use approximate optimistic augmentation instead of Laplacian SVM | Smoking Cessation semi-supervised prediction · Smoking Cessation, 50 missing test labels | accuracy: 88% vs. 73.5% | Helped |
| Use approximate optimistic augmentation instead of Structural EM | Car Evaluation classification · Car Evaluation | accuracy: 87.5% vs. 69.38% | Helped |
| Use approximate rather than exact optimistic augmentation | Bayesian network imputation · Breast Cancer | imputation accuracy: 84.38% vs. 80% | No clear effect |
Closest papers by meaning
- Extendable and Iterative Structure Learning Strategy for Bayesian Networks (ICLR 2025 · similarity 0.87)
- Learning Bayesian Networks with Low Rank Conditional Probability Tables (NeurIPS 2019 · similarity 0.87)
- Learning the Parameters of Bayesian Networks from Uncertain Data (AAAI 2021 · similarity 0.87)
- Learning Bayesian Networks in the Presence of Structural Side Information (AAAI 2022 · similarity 0.87)
- Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data (NeurIPS 2019 · similarity 0.86)
- DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks (NeurIPS 2020 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.