Rational Kriging
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916689561518080 |
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| author | Joseph, V. Roshan |
| author_facet | Joseph, V. Roshan |
| contents | This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimate of the mean from rational kriging is much more well behaved than that from ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. Its potential applications in emulation and calibration of computer models are also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05372 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Rational Kriging Joseph, V. Roshan Methodology Machine Learning This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimate of the mean from rational kriging is much more well behaved than that from ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. Its potential applications in emulation and calibration of computer models are also discussed. |
| title | Rational Kriging |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2312.05372 |