Learning graphical model parameters with approximate marginal inference
Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals, taking into account both model and inference appr...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 35(2013), 10 vom: 01. Okt., Seite 2454-67 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2013
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Online verfügbar |
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