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Gaussian Processes for Global Optimization

An implementation of Osborne et al. "Gaussian Processes for Global Optimization" by Lukas Radke, Aiko Pipo and Oliver Atanaszov.

This paper aims at optimizing black-box objective functions which are expensive to evaluate by using Gaussian Processes and Bayesian Inference.

Our implementation supports:

  • periodic/non-periodic kernels
  • optimization of functions with noisy observations
  • hyperparameter sampling and marginalization (using MCMC)

See "demo.ipynb" and "demo-experiments.ipynb" for example usage.

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