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/*!
\page arx Example of ARX model estimation

Here, we use the \c ARX class to estimate parameters and structure.
ARX model is defined as follows:
\f[
y_t = \theta' \psi_t + \rho e_t 
\f]
where \f$y_t\f$ is the system output, \f$[\theta,\rho]\f$ is vector of unknown parameters, \f$\psi_t\f$ is an 
vector of data-dependent regressors, and noise \f$e_t\f$ is assumed to be Normal distributed \f$\mathcal{N}(0,1)\f$.

Special cases include:...

\section math Mathematical background:
This particular model belongs to the exponential family, hence it has conjugate distribution of the Gauss-inverse-Wishart form (class egiw). See, [reference] for details.

For this model, structure estimation is a form of model selection procedure.
Specifically, we compare hypotheses that the data were generated by the full model with hypotheses that some regressors in vector \f$\psi\f$ are redundant. The number of possible hypotheses is then the number of all possible combinations of all regressors.

\section soft Software implementation:
Estimation with this class of model is perfromed by class ARX which is derived from class BMEF (estimation of exponential family).
The posterior density ( ARX::_epdf() ) is class egiw, which represents Gauss-inverse-Wishart density.

Structure estimation is implemented in method ARX::structure_est() which uses brute force tree search approach.

\section exa Examples of Use:

There are many ways how to use the object.
- Pure C++, as it is used in unit testing of the class arx, \subpage arx_test.cpp
- C++ application with UI configuration file, \subpage arx_test_ui
- Matlab interface, \subpage arx_matlab

*/