gpytorch
https://github.com/cornellius-gp/gpytorch
Python
A highly efficient implementation of Gaussian Processes in PyTorch
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- Issues
- Account for model and feature batches when detecting kernel diagonals
- Preserve periodic and cosine kernel second derivatives at coincident points
- Fix sum_interaction_terms with the default max_degree
- Do not squeeze batch dimensions of size one of the mean cache
- Sum the log densities of the standard deviations in LKJCovariancePrior
- Register the concentration of LKJ priors as a buffer
- Update the base distribution of transformed priors in _apply
- Support diag=True in IndexKernel
- Select the `active_dims` of the data kernel in `MultitaskKernel`
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