The online HTML version of these docs:

<http://rmcantin.bitbucket.org/html/>

+Bayesian optimization uses a distribution over functions to build a

+model of the unknown function for we are looking the extrema, and then

+apply some active learning strategy to select the query points that

+provides most potential interest or improvement. Thus, it is a

+sampling efficient method for nonlinear optimization, design of

+experiments or bandits-like problems.

+Getting and installing BayesOpt

+-------------------------------

+The library can be download from any of this sources:

- Download: <https://bitbucket.org/rmcantin/bayesopt>

- Mirror: <https://github.com/rmcantin/bayesopt>

+- Mirror: <http://mloss.org/software/view/453/>

-Bayesian optimization uses a distribution over functions to build a

-metamodel of the unknown function for we are looking the extrema,

-and then apply some active learning strategy to select the query

-points that provides most potential interest for the seek. For that

-reason, it has been traditionally intended for optimization of

-expensive function. However, the efficiency of the library make it

-also interesting for many types of functions.

+The install guide for Windows, Linux and MacOS:

+- [Install guide](http://rmcantin.bitbucket.org/html/install.html) or \ref install

-It is intended to be both fast and clear for development and

-research. At the same time, it does everything the "right way". For

+For a complete description of supported systems:

+- [Supported OS, compilers, versions...](https://bitbucket.org/rmcantin/bayesopt/wiki/Compatibility)

-- latin hypercube sampling is used for the preliminary design step,

-- extensive use of Cholesky decomposition and related tecniques to

- improve numeral stability and reduce computational cost,

-- kernels, criteria and parametric functions can be combined to

- produce more advanced functions,

-The documentation can be found in:

-- [Install guide](http://rmcantin.bitbucket.org/html/install.html) and \ref install

-- [Reference manual](http://rmcantin.bitbucket.org/html/reference.html) and \ref reference

-- [Bayesian optimization](http://rmcantin.bitbucket.org/html/bopttheory.html) and \ref bopttheory

-- [Models and functions](http://rmcantin.bitbucket.org/html/modelopt.html) and \ref modelopt

-- [Demos and examples](http://rmcantin.bitbucket.org/html/demos.html) and \ref demos

-- [Supported OS, compilers, versions...](https://bitbucket.org/rmcantin/bayesopt/wiki/Compatibility)

+If you just want to use BayesOpt as a library for nonlinear optimization:

+- [Reference manual](http://rmcantin.bitbucket.org/html/reference.html) or \ref reference

+- [Demos and examples](http://rmcantin.bitbucket.org/html/demos.html) or \ref demos

+If you want to understand what is Bayesian optimization:

+- [Bayesian optimization](http://rmcantin.bitbucket.org/html/bopttheory.html) or \ref bopttheory

+- [Models and functions](http://rmcantin.bitbucket.org/html/modelopt.html) or \ref modelopt

+The best place to ask questions and discuss about BayesOpt is the [bayesopt-discussion mailing list](https://groups.google.com/forum/#!forum/bayesopt-discussion). Alternatively, you may directly contact Ruben Martinez-Cantin <rmcantin@unizar.es>.

+Please file bug reports at: https://bitbucket.org/rmcantin/bayesopt/issues

You can also find more details at the [proyect

wiki](http://bitbucket.org/rmcantin/bayesopt/wiki/Home) or subscribe

to the [bayesopt-discussion mailing

list](https://groups.google.com/forum/#!forum/bayesopt-discussion).

-**Important:** This code is free to use. However, if you are using the

-library, specially if it is for research or academic purposes, please

-send me an email at <rmcantin@unizar.es> with your name, institution

-and a brief description of your interest for this code (one or two

-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. Cite BayesOpt as something like:

+Using BayesOpt for academic or commercial purposes

+--------------------------------------------------

-----------------------------------------------------------------------

+This code is licensed under the GPL and it is free to use. However,

+please consider these recomentations when using BayesOpt:

-Ruben Martinez-Cantin, **BayesOpt: a toolbox for

-nonlinear-optimization, experimental design and stochastic bandits**,

-<http://bitbucket.org/rmcantin/bayesopt>

+- If you are using the library for research or academic purposes,

+please send me an email at <rmcantin@unizar.es> with your name,

+institution and a brief description of your interest for this code

+- 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. If you use a specific algorithm, please also cite

+the corresponding work. The reference for each specific algorithm can

+be found in the documentation. Cite BayesOpt as something like:

+> Ruben Martinez-Cantin, **BayesOpt: a toolbox for

+> nonlinear-optimization, experimental design and stochastic bandits**,

+> <http://bitbucket.org/rmcantin/bayesopt>

+Commercial applications may also adquire a commercial license which

+allows more flexible terms than GPL. Please contact

+<rmcantin@unizar.es> for details.

----------------------------------------------------------------------