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 BayesOpt: A Bayesian optimization toolbox            {#mainpage}
 =========================================
 
-This code is still in **beta**. The release code will appear in
-http://bitbucket.org/rmcantin/bayesopt
-
-For any question, comment or to be informed about the release time,
-please subscribe to the discussion list
-----------------------------------------------------------------------
-
-This is an efficient, C++ implementation of the Bayesian
+BayesOpt is an free, efficient, implementation of the Bayesian
 optimization methodology for nonlinear-optimization, experimental
 design and stochastic bandits. In the literature it is also called
 Sequential Kriging Optimization (SKO) or Efficient Global
 The online HTML version of these docs:
 <http://rmcantin.bitbucket.org/html/>
 
-Basically, it uses a distribution over functions to build a
+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
 research. At the same time, it does everything the "right way". For
 example:
 
-- latin hypercube sampling is use for the preliminary sampling step
-- kernel parameters are trained with the preliminary samples and
-  fixed afterwards to avoid bias and divergence
-- matrix algebra tricks are used to guarantee that any covariance
-  matrix remains SPD and reduce computational cost.
+- 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,
 - etc.
 
-Originally, it was developed for as part of a robotics research
-project \cite MartinezCantin09AR \cite MartinezCantin07RSS, where a
-Gaussian process with hyperpriors on the mean and signal covariance
-parameters. Then, the metamodel was constructed using the Maximum a
-Posteriory (MAP) of the parameters.
-
-However, the library now has grown to support many more surrogate
-models, with different distributions (Gaussian processes,
-Student's-t processes, etc.), with many kernels and mean
-functions. It also provides different criteria (even some combined
-criteria) so the library can be used to any problem involving some
-bounded optimization, stochastic bandits, active learning for
-regression, etc.
-
-Start by reading the \ref install and the \ref reference
+Start by reading the \ref install and the \ref reference. You
+can also check about \ref bopttheory.
 
 You can also find more details at:
-<http://bitbucket.org/rmcantin/bayesian-optimization/wiki/Home>
+<http://bitbucket.org/rmcantin/bayesopt/wiki/Home>
 
-**Important:** This code is free to use. However, if you are using,
-or plan to use, 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 lines).
+**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
+lines).
 
-If you use BayesOpt in work that leads to a publication, we would
-appreciate it if you would kindly cite BayesOpt in your
+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:
 
 ----------------------------------------------------------------------
 
 ----------------------------------------------------------------------
 
-Copyright (C) 2011-2012 Ruben Martinez-Cantin <rmcantin@unizar.es>
+Copyright (C) 2011-2013 Ruben Martinez-Cantin <rmcantin@unizar.es>
 
 BayesOpt is free software: you can redistribute it and/or modify it
 under the terms of the GNU General Public License as published by the