Research map / Learning theory and algorithms / Sample complexity
Computing Divergences between Discrete Decomposable Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use JFComp instead of mcgo to compute KL divergence between Bayesian networks | KL divergence computation · bnlearn networks: earthquake, survey, asia, sachs, child, insurance, alarm, hailfinder, hepar2, win95pts | runtime (secs): earthquake: 0.0104 -> 0.0075; survey: 0.0140 -> 0.0081; asia: 0.0163 -> 0.0137; sachs: 0.0464 -> 0.0151; child: 0.0778 - | Helped |
| Use JFComp instead of mcgo to compute KL divergence between Bayesian networks | KL divergence computation · bnlearn networks: cancer, water, mildew | runtime (secs): cancer: 0.0117 -> 0.0132; water: 6.9326 -> 7.6454; mildew: 19.326 -> 19.459 | Hurt |
| Choose the divergence used to compare candidate models | candidate Bayesian networks A and B · sachs model selection case study | candidate ranking: Table 2 run 1: KL 0.4021 vs 0.5169; Hellinger 0.3027 vs 0.2915. Table 3: KL 0.3687 vs 0.3090; Hellinger 0.3013 vs 0.2921 | Mixed |
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