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.