Comments (2)
chris-nemeth
commented on June 30, 2024
Currently, GPy is more developed than GaussianProcesses.jl. In particular, GPy can handle non-Gaussian likelihoods, which are useful for classification type problems. We're currently in the process of extending the functionality of GaussianProcesses.jl and hopefully by Christmas, we should have the main features of GPy implemented.
In terms of regression, I think we have about the same functionality as GPy, so there's not much difference here. However, some preliminary tests show that GaussianProcesses.jl is faster than GPy #37 and this is something that we're consciously trying to improve upon.
from gaussianprocesses.jl.
aus70
commented on June 30, 2024
Very good, thanks!
from gaussianprocesses.jl.
Related Issues (20)
- compatible issue HOT 1
- gradient and Hessian
- Compatibility with Flux v0.12 and Zygote v0.6
- "Params not defined" when loading GaussianProcesses package HOT 2
- New release? HOT 4
- @JuliaRegistrator register HOT 4
- Translate sklearn to Julia request HOT 1
- Compute kernel matrix explicitly HOT 1
- Contribute to Wikipedia's Comparison of Gaussian process software HOT 1
- Error when trying to optimize GPR example HOT 1
- Training data with known uncertainties HOT 3
- Optimization of hyper parameters HOT 2
- optimize! MethodError HOT 1
- strange underestimations of noise around training points HOT 1
- Dependency error when trying to install the package HOT 1
- Optimization HOT 3
- Example Request for MeanLin HOT 2
- Worse performance after `optimize!(gpe)` HOT 2
- Is it possible to use GaussianProcesses to fit an ongoing stream of data? HOT 5
- MeanLin MethodError? HOT 4
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from gaussianprocesses.jl.