not included by default in the linker, Python or Matlab paths by
default. This is specially critical when building shared libraries
(mandatory for Python usage). The script \em exportlocalpaths.sh makes
-sure that the folder is included in all the necessary paths.
+sure that the folder with the libraries is included in all the
After that, there are 3 steps that should be follow:
\li Define the function to optimize.
\section params Understanding the parameters
BayesOpt relies on a complex and highly configurable mathematical
-model. Also, the key to nonlinear optimization is to include as much
-knowledge as possible about the target function or about the
-problem. Or, if the knowledge is not available, keep the model as
-general as possible (to avoid bias).
+model. In theory, it should work reasonably well for many problems in
+its default configuration. However, Bayesian optimization shines when
+we can include as much knowledge as possible about the target function
+or about the problem. Or, if the knowledge is not available, keep the
+model as general as possible (to avoid bias). In this part, knowledge
+about Gaussian process or nonparametric models in general might be
+For example, with the parameters we can select the kind of kernel,
+mean or surrogate model that we want to use. With the kernel we can
+play with the smoothness of the function and it's derivatives. The
+mean function can be use to model the overall trend (is it flat?
+linear?). If we know the overall signal variance we better use a
+Gaussian process, if we don't, we should use a Student's t process
For that reason, the parameters are bundled in a structure or
dictionary, depending on the API that we use. This is a brief