The Bayesian Filtering Library development team is pleased to announce the first bug-fix release of the Bayesian Filtering Library v0.6.
You can download this release from and read the installation instructions online at (also reachable through the orocos website).

The following important bug was fixed in this release:

ID Summary
440 Boost-wrapper's Rowvector * ColumnVector implementation wrong!

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.