Bayesian Filtering Library 0.6.0 released

The Bayesian Filtering Library development team is pleased to announce the=
=20
0.6.0 release of BFL.
You can download this release from <http://www.orocos.org/bfl/source> and r=
ead=20
the installation instructions online at=20
<http://people.mech.kuleuven.be/~tdelaet/bfl_doc/installation_guide> (also=
=20
reachable through the orocos website <www.orocos.org/bfl>).

This release includes support for lti, boost and newmat as matrix library a=
nd=20
lti and boost as random number generator.
A new feature is the backward filter and smoother algorithm and the CPPUnit=
=20
tests.
=46urthermore for the first time, a step-by-step installation guide is avai=
lable=20
for Visual Studio on Windows.

In detail this release addresses the following reported issues:


=A0 ID =A0 =A0 =A0 =A0 =A0 =A0Summary=20
=A0 303 =A0 =A0The future of BFL (aka: BFL needs new maintainer)=20
=A0 319 =A0 =A0add backward filter and tests to build system=20
=A0 320 =A0 =A0Default implementation for virtual functions=20
=A0 321 =A0 =A0const function arguments in mcpdf class=20
=A0 329 =A0 =A0Add function to get one sample + change int into unsigned...=
=20
=A0 330 =A0 =A0Sample::ValueSet() does not adjust dimension=20
=A0 331 =A0 =A0BFL should use return codes or c++ exceptions=20
=A0 333 =A0 =A0Sample stores dimension=20
=A0 334 =A0 =A0No need to re-implement virtual functions=20
=A0 335 =A0 =A0Cleanup of some pdf code=20
=A0 343 =A0 =A0PostGet() should return a more specific Pdf if possible=20
=A0 349 =A0 =A0Add SVN revision number to doxygen generated docu=20
=A0 350 =A0 =A0make analytic system and measurement model consistent=20
=A0 351 =A0 =A0Extension for IteratedExtendedKalmanFilter=20
=A0 389 =A0 =A0Examples refuse to compile=20
=A0 392 =A0 =A0Change build system to cmake=20
=A0 393 =A0 =A0Not possible to build static libraries=20
=A0 395 =A0 =A0Automate building of Ubuntu/Debian packages=20
=A0 400 =A0 =A0Cholesky decomposition=20
=A0 403 =A0 =A0Building BFL in Windows=20
=A0 411 =A0 =A0Boost needs pinv implementation=20
=A0 416 =A0 =A0License issues for BFL template code

Details are available at:=20
<https://www.fmtc.be/orocos-bugzilla/buglist.cgi?bug_file_loc=3D&bug_file_l=
oc_type=3Dallwordssubstr&bug_id=3D&bugidtype=3Dinclude&chfieldfrom=3D&chfie=
ldto=3DNow&chfieldvalue=3D&component=3Dbuild%20system&component=3Dcore&emai=
l1=3D&email2=3D&emailassigned_to1=3D1&emailassigned_to2=3D1&emailcc2=3D1&em=
ailreporter2=3D1&emailtype1=3Dsubstring&emailtype2=3Dsubstring&field-1-0-0=
=3Dcomponent&field-1-1-0=3Dproduct&field-1-2-0=3Dresolution&field-1-3-0=3Dt=
arget_milestone&field0-0-0=3Dnoop&long_desc=3D&long_desc_type=3Dsubstring&p=
roduct=3DBFL&query_format=3Dadvanced&remaction=3D&resolution=3DFIXED&short_=
desc=3D&short_desc_type=3Dallwords&target_milestone=3D---&type-1-0-0=3Danye=
xact&type-1-1-0=3Danyexact&type-1-2-0=3Danyexact&type-1-3-0=3Danyexact&type=
0-0-0=3Dnoop&value-1-0-0=3Dbuild%20system%2Ccore&value-1-1-0=3DBFL&value-1-=
2-0=3DFIXED&value-1-3-0=3D---&value0-0-0=3D&votes=3D&query_based_on=3D>.


The Bayesian Filtering Library (BFL) provides an application independent=20
framework for inference in Dynamic Bayesian Networks, i.e., recursive=20
information processing and estimation algorithms based on Bayes' rule, such=
=20
as (Extended) Kalman Filters, Particle Filters (or Sequential Monte Carlo=20
methods), etc. These algorithms can, for example, be run on top of the=20
Realtime Services, or be used for estimation in Kinematics & Dynamics=20
applications.


Kind regards,

Tinne De Laet
-- 
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