TutorialGP

KO | EN

gitlite — search

#javascript #python #hacktoberfest #ai #android #docker #macos #java #nodejs #kubernetes #awesome-list #security #ios #open-source

(★ 67)

Tutorial on Gaussian Processes

TutorialGP Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# [Tutorial on Gaussian Processes](https://maziarraissi.github.io/TutorialGP/)

This is a short tutorial on Gaussian Processes, Multi-fidelity Gaussian Processes, Gaussian Processes for Differential Equations, and Bayesian Optimization.

For more details, please refer to the following references: (https://maziarraissi.github.io/TutorialGP/)

1) Rasmussen, Carl Edward. "[Gaussian processes for machine learning](http://www.gaussianprocess.org/gpml/)." (2006).

2) Raissi, Maziar, and George Karniadakis. "[Deep Multi-fidelity Gaussian Processes](https://arxiv.org/abs/1604.07484)." arXiv preprint arXiv:1604.07484 (2016).

3) Raissi, Maziar, and George Em Karniadakis. "[Machine Learning of Linear Differential Equations using Gaussian Processes](https://arxiv.org/abs/1701.02440)." arXiv preprint arXiv:1701.02440 (2017).

4) Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "[Inferring solutions of differential equations using noisy multi-fidelity data](https://arxiv.org/abs/1607.04805)." arXiv preprint arXiv:1607.04805 (2016).

5) Shahriari, Bobak, et al. "[Taking the human out of the loop: A review of bayesian optimization](https://ieeexplore.ieee.org/abstract/document/7352306/)." Proceedings of the IEEE 104.1 (2016): 148-175.

TutorialGP

TutorialGP Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

TutorialGP

TutorialGP Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README