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.

Tinne, the BFL maintainer.
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