The Bayesian Filtering Library development team is pleased to announce the 0.6.0 release of BFL.
You can download this release from here and read the installation instructions online (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.
In detail this release addresses the following reported issues:
ID Summary 303 The future of BFL (aka: BFL needs new maintainer) 319 add backward filter and tests to build system 320 Default implementation for virtual functions 321 const function arguments in mcpdf class 329 Add function to get one sample + change int into unsigned... 330 Sample::ValueSet() does not adjust dimension 331 BFL should use return codes or c++ exceptions 333 Sample stores dimension 334 No need to re-implement virtual functions 335 Cleanup of some pdf code 343 PostGet() should return a more specific Pdf if possible 349 Add SVN revision number to doxygen generated docu 350 make analytic system and measurement model consistent 351 Extension for IteratedExtendedKalmanFilter 389 Examples refuse to compile 392 Change build system to cmake 393 Not possible to build static libraries 395 Automate building of Ubuntu/Debian packages 400 Cholesky decomposition 403 Building BFL in Windows 411 Boost needs pinv implementation 416 License issues for BFL template code
Details are available through: this link
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.