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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625033/ https://www.ncbi.nlm.nih.gov/pubmed/34828243 http://dx.doi.org/10.3390/e23111545 |