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AugmentedGaussianProcesses.jl is a Julia package in development for Data Augmented Sparse Gaussian Processes. It contains a collection of models for different gaussian and non-gaussian likelihoods, which are transformed via data augmentation into conditionally conjugate likelihood allowing for extremely fast inference via block coordinate updates. There are also more options to use more traditional variational inference via quadrature or Monte Carlo integration.
Features
- Two GP classification likelihoods
- Four GP Regression likelihoods
- Two GP event counting likelihoods
- One Multi-Class Classification Likelihood
- Multi-Ouput models
- More models in development
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Categories
Data VisualizationLicense
MIT LicenseFollow AugmentedGaussianProcesses.jl
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