Bayesian Probabilistic Numerical Methods

By Jon Cockayne, Chris J. Oates, T. Sullivan, Mark A. Girolami. 2017

182 citations recorded by the source corpus.

Over forty years ago average-case error was proposed in the applied mathematics literature as an alternative criterion with which to assess numerical methods. In contrast to worst-case error, this ...

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yibo Yang
2. Paris Perdikaris
3. G. Em Karniadakis
4. Maizar Raissi
5. Stefania Fresca
6. Andrea Manzoni
7. Liu Yang
8. Dongkun Zhang

In Statistics and Probability

1. Yibo Yang
2. Paris Perdikaris
3. G. Em Karniadakis
4. Sifan Wang
5. Jian-Xun Wang
6. Luning Sun
7. Han Gao

Citations in the corpus

• Adversarial Uncertainty Quantification in Physics-Informed Neural Networks (2018, methodology): “In contrast to previous approaches to inferring solutions of partial differential equations from data [44, 45, 46, 47], the proposed methodology does not rely on Gaussian assumptions, and it can directly tackle nonlinear problems without any need for linearization.”