BayesOpt /

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Python install in site-packages. No need to add anything to Pythonpath
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BayesOpt: A Bayesian optimization library

BayesOpt is an efficient implementation of the Bayesian optimization methodology for nonlinear optimization, experimental design and hyperparameter tunning.

Bayesian optimization uses a distribution over functions to build a surrogate model of the unknown function for we are looking the optimum, and then apply some active learning strategy to select the query points that provides most potential interest or improvement. Thus, it is a sample efficient method for nonlinear optimization, design of experiments and simulations or bandits-like problems. Currently, it is being used in many scientific and industrial applications. In the literature it is also called Sequential Kriging Optimization (SKO), Sequential Model-Based Optimization (SMBO) or Efficient Global Optimization (EGO).

BayesOpt is licensed under the AGPL and it is free to use. However, if you use BayesOpt in a work that leads to a scientific publication, we would appreciate it if you would kindly cite BayesOpt in your manuscript.

Ruben Martinez-Cantin, BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits. Journal of Machine Learning Research, 15(Nov):3735--3739, 2014.

The paper can be found at

Commercial applications may also acquire a commercial license. Please contact for details.

Getting and installing BayesOpt

The library can be download from any of this sources:

You can also get the cutting-edge version from the repositories:

>> hg clone

or the git mirror:

>> git clone

The online documentation can be found at: where it includes a install guide.

Questions and issues

Copyright (C) 2011-2015 Ruben Martinez-Cantin

BayesOpt is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

BayesOpt is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with BayesOpt. If not, see