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To begin I tried logging in with GitHub and also creating an account on the pyro forums, but neither of those is working.
### Problem
I need to fit a batch of four independent Gaussian Processes…
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Gaussian Processes over spatial distances currently only use the exponential covariance kernel $\text{exp}(\rho \cdot D_{ij})$ where $D_{ij}$ is the scaled distance between taxa $i$ and $j$.
It wou…
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It would be great if `rhine-bayes` had an implementation of Gaussian processes, somehow parametrized by their covariance. I know too little about how to sample from such a process.
CC @reubenharry
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A lonely TODO wait for his programmer savior within slicingmodel.jl
It seems to be about adding interval propagation.
On the same file, there seems to be a performance issue with the optimisation …
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**What is the main topic of this tutorial**: Gaussian Processe with scikit-learn or George
**Learning goals**
Examples of gaussian processes regression, learn to how properly control the errors, …
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It seems that sklearn does not train the parameters.
Maybe use pytorch instead?
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A while ago, we aimed at making GPs in Shogun work with StringFeatures. I don't know the current state on this.
This task is to
- make sure GPs can be used with String features
- write a minimal exa…
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Hi Kaspar,
Thank you so much for sharing your code! I have implemented a tensorflow version and it works well on sin functions. However, when it comes to the functions sampled from gaussian processes…
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A simple linear kernel is lacking in Gaussian Processes, that is `K(x, x') = (x - c)(x' - c)
From http://www.cs.toronto.edu/~duvenaud/cookbook/index.html it is useful in modelling linear functions …