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Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design
Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516703/ https://www.ncbi.nlm.nih.gov/pubmed/33286031 http://dx.doi.org/10.3390/e22020258 |