A Novel Qualitative Maximum a Posteriori Estimation for Bayesian Network Parameters Based on Computing the Center Point of Constrained Parameter Regions
Introducing parameter constraints has become a mainstream approach for learning Bayesian network parameters with small datasets. The QMAP (Qualitative Maximum a Posteriori) estimation has produced the best learning accuracy among existing learning approaches. However, the rejection-acceptance sampli...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9359729/ |