# Rational Kriging

arXiv: https://arxiv.org/abs/2312.05372 · HTML: https://mlsanity.com/paper/2312.05372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [SPlit: An Optimal Method for Data Splitting](https://mlsanity.com/paper/2012.10945/) (2020)
- [A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties](https://mlsanity.com/paper/1508.07155/) (2015)
- [Orthogonal Gaussian process models](https://mlsanity.com/paper/1611.00203/) (2016)
- [Efficient Calibration for Imperfect Computer Models](https://mlsanity.com/paper/1507.07280/) (2015)
- [Function-on-function kriging, with applications to 3D printing of aortic tissues](https://mlsanity.com/paper/1910.01754/) (2019)
- [A Frequentist Approach to Computer Model Calibration](https://mlsanity.com/paper/1411.4723/) (2014)
- [Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction](https://mlsanity.com/paper/1707.08215/) (2017)
- [Adjustments to Computer Models via Projected Kernel Calibration](https://mlsanity.com/paper/1705.03422/) (2017)

## Built on by (most cited first, 1 of 1)

- [Deep P-Spline: Theory, Fast Tuning, and Application](https://mlsanity.com/paper/2501.01376/) (2025)

## Abstract (arXiv)

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.