The Bayesian Filtering Library development team is pleased to announce the 0.7.0 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.
New features include:
* RTT toolkit for BFL (now supporting boost)
* uniform probability density functions
* mixture probability density functions
* histogram filters
* mixture particle filters

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.