Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
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MDPI AG
2021
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| Accès en ligne: | https://doaj.org/article/7279de62091e4c17b2e769ba1c8ad513 |
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