The Bayesian Filtering Library development team is pleased to announce
the 0.8.0 release of BFL.
You can download this release from http://www.orocos.org/bfl/source
and read the installation instructions
http://people.mech.kuleuven.be/~tdelaet/bfl_doc/installation_guide/
(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.
The new release contains beside bug fixes, adaptations to the matrix
wrapper to allow a new RTT 2.0 toolit for BFL.

Details are available through:
https://www.fmtc.be/bugzilla/orocos/buglist.cgi?query_format=advanced&sh...

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.
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