Bayesian Filtering Library 0.6.0 prereleased

The Bayesian Filtering Library development team is pleased to announce the 
0.6.0 prerlease of BFL. You can download this prerelease from 
<http://www.orocos.org/bfl/source> 

and read the installation instructions on 
<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. 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.

Kind regards from the BFL-maintainer,

Tinne
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