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Gaussian ProcessesBayesian 2025. 6. 26. 23:54
https://distill.pub/2019/visual-exploration-gaussian-processes/
A Visual Exploration of Gaussian Processes
How to turn a collection of small building blocks into a versatile tool for solving regression problems.
distill.pub
https://infallible-thompson-49de36.netlify.app/
https://infallible-thompson-49de36.netlify.app/
Gaussian processes (GPs) are the canonical method for Bayesian modeling of functions, especially in applications, where data is scarce. A GP combines flexible modeling with uncertainty quantification, which is valuable in many downstream tasks. GPs are pro
infallible-thompson-49de36.netlify.app
https://nbviewer.org/github/adamian/adamian.github.io/blob/master/talks/Brown2016.ipynb
Jupyter Notebook Viewer
Joint¶ Let's start with a multivariate Gaussian. Assume that we have a random variable $\mathbf{f}$ which follows a multivariate Gaussian, and we partition its dimensions into two sets, $A,B$. Then, the joint distribution can be written as: $$ p(\underbra
nbviewer.org
https://www.youtube.com/watch?v=ICXigKDGBMY&list=PLzZ7PPT4KK5qpd-1VF4qmFMlpnr1is7Pu&index=18
https://www.youtube.com/watch?v=exqpaqaPG2M
GPyTorch
gpytorch.ai
https://gaussianprocess.org/gpml/chapters/RW.pdf
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