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61<h1>Bayesian Decision-Making toolbox for C++</h1>
62<p>
63<h3 align="center">1 </h3><dl class="version" compact><dt><b>Version:</b></dt><dd>0.1</dd></dl>
64<dl class="author" compact><dt><b>Author:</b></dt><dd>Vaclav Smidl</dd></dl>
65BDM is a collection of methods for selected tasks of Bayesian decision-making, such as estimation, filtering and control.<h2><a class="anchor" name="fea">
66Features</a></h2>
67At present the following algorithms are implemented:<ul>
68<li><b>Bayesian</b> <b>filtering</b> : Kalman filter, EKF, patricle filter (PF),<ul>
69<li>these can be combined mutualy together in mode demanding schemes, see marginalized particle filter MPF</li></ul>
70</li></ul>
71<p>
72<ul>
73<li><b>Classification</b> using mixtures of exponential famiuly models (MixEF),</li><li><b>Density</b> <b>estimation</b> : using mixtures (MixEF), density composition (merger)</li></ul>
74<h2><a class="anchor" name="down">
75Download and Use</a></h2>
76The library is available under GPL, see installation instructions on page <a class="el" href="install.html">How to install BDM</a><p>
77Precompiled Mex files for use within Matlab are available <a class="el" href="mexfiles.html">How to write and use mex files for Matlab</a><h2><a class="anchor" name="app">
78Design Approach</a></h2>
79The toolbox is designed using <b>object-oriented</b> approach with two design criteria: <ul>
80<li>relation to mathematics, </li>
81<li>efficient evaluation, while the first one is more important than the latter.</li>
82</ul>
83Hence, 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.<p>
84OpenMP is used to achive efficient implementation on parallel architectures.<h2><a class="anchor" name="impl">
85Implementation</a></h2>
86BDM is build on top of <code>IT++</code> 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:<p>
87<ul>
88<li>computational speed comparable to built-in Matlab function, and surpassing interpreted Matlab in order of magnitudes, </li>
89<li>native support for object-oriented programming, </li>
90<li>support for templates which is often more appropriate than object-oriented programming, </li>
91<li>cross-platform compatibility. </li>
92</ul>
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