\begin{Desc}
\item[Version:]0.1\end{Desc}
\begin{Desc}
\item[Author:]Vaclav Smidl\end{Desc}
BDM is a collection of methods for selected tasks of Bayesian decision-making, such as estimation, filtering and control.\hypertarget{index_Approach}{}\section{Approach}\label{index_Approach}
The toolbox is designed using two design criteria: \begin{itemize}
\item relation to mathematics, \item efficient evaluation, while the first one is more important than the latter.\end{itemize}
Hence, each mathematical object such as probability density is represented by one software object. The resulting algorithms are then implemented as operations on these objects. In cases when more efficient solution can be achived when this structure is not respected, a parallel implementation is created and clearly marked as specific.\hypertarget{index_Methods}{}\section{Methods}\label{index_Methods}
At present we support the following methods: \begin{itemize}
\item Estimation: ARX models \item Filtering: Kalman filtering, Extended Kalman filters (EKF), Particle filters (PF), Marginalized Particle filters (MPF). \item Desing: None.\end{itemize}
\hypertarget{index_Implementation}{}\section{Implementation}\label{index_Implementation}
BDM is build on top of {\tt IT++} which wraps numerically efficient operations of linear algebra into easy to use C++ classes. Thanks to this excellent library, writing of numerical algorithms is as easy as in Matlab but we gain significant advantages:

\begin{itemize}
\item computational speed comparable to built-in Matlab function, and surpassing interpreted Matlab in order of magnitudes, \item native support for object-oriented programming, \item support for templates which is often more appropriate than object-oriented programming, \item cross-platform compatibility. \end{itemize}
