Bayesian Filtering Library 0.6.0 released

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.

Dev C++

Hi Friends:

Do you know how to configure Dev C++ (under Windows) to use with the BFL Library? Please any input will help.

Have a nice day,

Aldo Camargo

Bayesian Filtering Library 0.6.0 released

Since the links in the announcement of the BFL 0.6.0 release were not visible
in the previous email, I hereby send you the completed announcement.

Kind regards,

Tinne

The Bayesian Filtering Library development team is pleased to announce the
0.6.0 release of BFL.
You can download this release from and read
the installation instructions online at
(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 at:
.

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.
_______________________________________________
I hereby promise not to top-post on the
BFL mailing list
BFL [..] ...
http://lists.mech.kuleuven.be/mailman/listinfo/bfl

Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

Bayesian Filtering Library 0.6.0 released

On Tuesday 12 June 2007 18:33:58 Tinne De Laet wrote:
> 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:
> _______________________________________________
> I hereby promise not to top-post on the
> BFL mailing list
> BFL [..] ...
> http://lists.mech.kuleuven.be/mailman/listinfo/bfl
>
> Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

--
_______________________________________

Tinne De Laet
Department of Mechanical Engineering - PMA
Katholieke Universiteit Leuven
Celestijnenlaan 300B - bus 2420
3001 Heverlee
Tel: +32 (0)16 32 25 33
http://people.mech.kuleuven.be/~tdelaet/

_______________________________________
_______________________________________________
I hereby promise not to top-post on the
BFL mailing list
BFL [..] ...
http://lists.mech.kuleuven.be/mailman/listinfo/bfl

Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm