# \[ANN\] AugmentedGaussianProcesses.jl

**URL:** https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952
**Category:** Package Announcements
**Created:** [April 9, 2019, 9:58am UTC](https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952 "2019-04-09T09:58:45Z")
**Posts on this page:** 4
**Page:** 1

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### Author: ![theogf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/theogf/32/1987_2.png) [@theogf](https://discourse.julialang.org/u/theogf)
#### Post date: [April 9, 2019, 9:58am UTC](https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952/1 "2019-04-09T09:58:45Z")

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Hi,  
Just want to make so shameless advertisement for my Gaussian Process package : [AugmentedGaussianProcesses.jl](https://github.com/theogf/AugmentedGaussianProcesses.jl)  
With it you can work efficiently with non-gaussian likelihood (as well as gaussian of course) while scaling with high number of data.  
It features :

- Non-Gaussian likelihoods : Student-T, Bernoulli (logistic link), Bayesian-SVM (hinge loss) and a multi-class likelihood (similar to softmax), and more incoming
- Scalability via inducing points (scalable to 1e6 points via stochastic updates)
- Inference via : Numerical Variational Inference (gradients approximated numerically), Analytic Variational Inference (via an augmentation trick!), Gibbs Sampling
- Hyperparameter optimization included (but to be improved)

Feature incoming :

- Online learning
- More kernel functions
- Integration of AD

Please check it out, any feedback is welcome 🙂  
PS: Also I am alone on this project so if anyone is interested to work on this as well, this would be amazing

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### Author: ![francesco.alemanno](https://avatars.discourse-cdn.com/v4/letter/f/e8c25b/32.png) [@francesco.alemanno](https://discourse.julialang.org/u/francesco.alemanno)
#### Post date: [June 28, 2019, 8:47am UTC](https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952/2 "2019-06-28T08:47:56Z")

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This is a fantastic package! Nice work

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### Author: ![theogf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/theogf/32/1987_2.png) [@theogf](https://discourse.julialang.org/u/theogf)
#### Post date: [June 28, 2019, 12:00pm UTC](https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952/3 "2019-06-28T12:00:41Z")

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Thanks! I appreciate!  
Are you using GP for your work? Tell me if there are any feature you are missing 🙂

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### Author: ![francesco.alemanno](https://avatars.discourse-cdn.com/v4/letter/f/e8c25b/32.png) [@francesco.alemanno](https://discourse.julialang.org/u/francesco.alemanno)
#### Post date: [July 1, 2019, 4:02pm UTC](https://discourse.julialang.org/t/ann-augmentedgaussianprocesses-jl/22952/4 "2019-07-01T16:02:06Z")

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Hi Theogf! yes i’m using it to perform regression on the results of a very noise MCMC simulation, perhaps some more examples on how different kernel produce different results, and the ability to load a trained GP from file.  
Anyway it is a very easy to use and powerful package, thank you again for your work!