The Bayesian Filtering Library development team is pleased to announce the 0.6.0 prerlease of BFL.
You can download this prerelease from

and read the installation instructions on
(also reachable through the orocos website)

This release includes support for lti, boost and newmat as matrix library and lti and boost as random number generator.
A new feature is the backward filter and smoother algorithm and the CPPUnit tests.
Furthermore for the first time, a step-by-step installation guide is available for Visual Studio on Windows.

The Bayesian Filtering Library (BFL) provides an application independent framework for inference in Dynamic Bayesian Networks, i.e., recursive information processing and estimation algorithms based on Bayes' rule, such as (Extended) Kalman Filters, Particle Filters (or Sequential Monte Carlo methods), etc. These algorithms can, for example, be run on top of the Realtime Services, or be used for estimation in Kinematics & Dynamics applications.