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/*!
\mainpage Bayesian Decision-Making toolbox for C++

\version 0.1

\author Vaclav Smidl

BDM is a collection of methods for selected tasks of Bayesian decision-making, such as estimation, filtering and control.
It is implemneted in C++ with available interfaces to matlab (called bdmtoolbox) and Python (preliminary).

\section fea Features
At present the following algorithms are implemented:
 - \b Bayesian \b filtering : Kalman filter, EKF, patricle filter (PF),
    - these can be combined mutualy together in mode demanding schemes, see marginalized particle filter MPF

 - \b Classification using mixtures of exponential famiuly models (MixEF),
 - \b Density \b estimation : using mixtures (MixEF), density composition (merger)
 - \b LQG \b control : so far only for ARX and Kalman filters

\section down Download and Use
The library is available under GPL, see installation instructions on page \ref install

It is split into library and applications. One of the applications is toolbox for matlab, which can be downloaded in binary form for win32, see \ref install.

\section app Publications

Publications that were made with the toolbox are:
 - \b Software papers:  Fusion 2010 
 - \b Distributed \b identification : <a href="http://library.utia.cas.cz/separaty/2009/AS/smidl-log-normal%20merging%20for%20distributed%20system%20identification.pdf"> SYSID 09 </a>
 - \b Baysian \b filtering : <a href="http://library.utia.cas.cz/separaty/2009/AS/smidl-challenges%20and%20limits%20of%20extended%20kalman%20filter%20based%20sensorless%20control%20of%20permanent%20magnet%20synchronous%20machine%20drives.pdf"> EPE 09 </a>
 
The status of replicability of the published experiments is available in \ref published.

\section impl Implementation

 BDM is build on top of \c IT++ which wraps numerically efficient operations of linear algebra into easy to use C++ classes with Matlab-like syntax.
 */