Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Learning the Parameters of Bayesian Networks from Uncertain Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Two-Node | average overall discrepancy (%): 8.70 -> 0.45 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Asia, X-Ray | average overall discrepancy (%): 2.61 -> 0.15 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Asia, Dyspnoea | average overall discrepancy (%): 1.76 -> 0.11 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Asia, Asia? | average overall discrepancy (%): 2.92 -> 0.22 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Asia, Smoker | average overall discrepancy (%): 1.10 -> 0.10 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Asia, 4 nodes | average overall discrepancy (%): 7.95 -> 0.17 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Child, Case 1 | average overall discrepancy (%): 1.12 -> 0.30 | Helped |
| Incorporate likelihood evidence instead of treating uncertain observations as deterministic values | EM parameter learning · Child, Case 2 | average overall discrepancy (%): 3.99 -> 0.34 | Helped |
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