Bayesian Decision-Making toolbox for C++

1

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).

Features

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

Download and Use

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

Publications that were made with the toolbox are:

• Software papers: Fusion 2010
• Distributed identification : SYSID 09
• Baysian filtering : EPE 09

The status of replicability of the published experiments is available in Status of experiments published in papers:.

Implementation

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.