Introduction

Welcome to the Bayesian-Optimization-with-Gaussian-Processes wiki!

This is a global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important.

This package was motivated by hyper-parameter optimization of machine leaning algorithms when performing cross validation. Some of the design choices were clearly made with this setting in mind and ultimately a out-of-the-box cross validation optimization object will be implemented (soon).