Web Reference: mization: Bayesian optimization. This method is particularly useful when the function to be optimized is expensive to evaluate, and we have n. information about its gradient. Bayesian optimization is a heuristic approach that is applicable to low-d. These demos are automatically compiled and installed with the library. They can be found in the /bin subfolder. The source code of these demos is in /examples While this tutorial is only intended to be a brief introduction to Bayesian Optimization, we hope that we have been able to convey the basic underlying ideas. Consider watching the lecture by Nando de Freitas [3] for an excellent exposition of the basic theory.
YouTube Excerpt: Bayesian Optimization
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