Bayesian Decision-Making toolbox for C++
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).
At present the following algorithms are implemented:
- Bayesian filtering : Kalman filter, EKF, patricle filter (PF),
- these can be combined mutualy together in mode demanding schemes, see marginalized particle filter MPF
- Classification using mixtures of exponential famiuly models (MixEF),
- Density estimation : using mixtures (MixEF), density composition (merger)
- LQG control : so far only for ARX and Kalman filters
The library is available under GPL, see installation instructions on page BDM Use - Installation
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 BDM Use - Installation.
Publications that were made with the toolbox are:
The status of replicability of the published experiments is available in Status of experiments published in papers:.
BDM is build on top of
IT++ which wraps numerically efficient operations of linear algebra into easy to use C++ classes with Matlab-like syntax.
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