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Gaussian Process package based on data augmentation, sparsity and natural gradients

Results 25 AugmentedGaussianProcesses.jl issues
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This pull request changes the compat entry for the `Optimisers` package from `0.1` to `0.1, 0.2`. This keeps the compat entries for earlier versions. Note: I have not tested your...

Allows for creating any augmented model given the likelihood

(Apologies if it seems like I've been spamming too many issues!) [BayesianOptimisation.jl](https://github.com/jbrea/BayesianOptimization.jl) is a neat little package built on top of [GaussianProcesses.jl](https://github.com/STOR-i/GaussianProcesses.jl) that provides support for Bayesian Optimisation, a common...

It seems like there are a few Julia packages for GPs (this one, Stheno.jl and GaussianProcesses.jl) and it is not quite clear what the differences/similarities are. The reference [here](https://theogf.github.io/AugmentedGaussianProcesses.jl/stable/comparison/#Julia-GP-Package-Comparison-1) does...

i am able to train using ``` model = SVGP(X_train, Y_train, kernel, LogisticLikelihood(), AnalyticVI(), num_inducing) ``` but get an error `ERROR: LoadError: MethodError: no method matching AnalyticSVI()` if i instead...

hello, i'm confused about how to make sure that all the hyperparameters of my kernel are being learned. in particular i would like to use a matern 5/2 kernel specified...

Hi, I am trying to run a GP multi-output regression with inducing points, however it seems the code is broken somewhere, here the code : using AugmentedGaussianProcesses const AGP =...

hello, thanks for the interesting package! i'm interested in running the polya gamma scheme for the Logistic and LogisticSoftMax likelihoods (for a baseline for a possible paper). i would like...

enhancement

If I change the kernel in [Regression - Gaussian.ipynb](https://github.com/theogf/AugmentedGaussianProcesses.jl/blob/master/examples/Regression%20-%20Gaussian.ipynb) to a `PeriodicKernel()`, I get the following error: ```julia Mutating arrays is not supported Stacktrace: [1] error(::String) at ./error.jl:33 [2] (::Zygote.var"#455#456")(::Nothing)...

I managed to train a multi-input single-output with no issues. Consider the fake data: ``` Julia x1 = -5:0.8:5 x2 = -5:0.8:5 xx1 = vec([i for i in x1, j...