Promoting teaching, research, applications and creation of knowledge in the domain of Belief Functions
Belief functions and Applications society (BFAS) was created to promote teaching,
research, applications and creation of knowledge in the
domain of belief functions, their extensions, and the links they can share with other
such as probability theory, imprecise probability theory, probability boxes, possibility theory, fuzzy sets, random sets, ...
The society was born in 2010 at the occasion of the first conference on the theory of belief functions, which was held in Brest, France, with Arthur P. Dempster and Glenn Shafer as honorary chairs and invited speakers.
The organization can use any means or technique adapted to the realization of its goals,
like establishing conventions with public, semi-public or private organizations,
with physical or moral persons. In particular, it supports the organization of a
biennial international conference and a
biennial international school both on belief functions and their applications.
If we trace them back to Arthur P. Dempster's works, belief functions were initially proposed as a way to achieve generalized Bayesian inference without priors. They are mathematically related to random sets.
In general, belief functions are used as
a way to model uncertainty where imprecision,
or lack of knowledge has to be modelled explicitly. As a matter-of-fact, they combine set representation and distribution
representation to do so.
The organization is composed of physical and moral persons. All members have the right to vote during the assemblies. Conventions may be contracted with other non-profit organization to establish reciprocal cooperation links, allowing their members to join with specific conditions.
New members are welcome. If you intend to do so, you may either participate to a BFAS conference or a BFAS school, membership being valid during 4 years, or contact us, members of the board will then be pleased to study your candidacy. Be aware that a quadrennial fee is asked (20€ at the moment).
A list of links to find software and toolboxes to facilitate the development and handling of belief functions.
|TBM||MATLAB||Philippe Smets||TBM, FMT|
|Software||R and MATLAB||Thierry Denœux||Clustering, Distance-based Classification, Approximation|
|iBelief||R||Kuang Zhou and Arnaud Martin||Some basic functions to implement belief functions.|
|Belief Package||R||Sébastien Destercke||Some basic functions to deal with discrete belief functions.|
|pyds||Python||Thomas Reineking||A Python library for performing calculations in the Dempster-Shafer theory of evidence.|
Professor, Compiègne University of Technology, France.
Associate professor, Artois University, France.
Scientist, Maritime Security, NATO STO Centre for Maritime Research and Experimentation, Italy.
Professor, Artois University, France.
Professor, School of Automation, Northwestern Polytechnical University Xi'an, China.
Professor of Artificial Intelligence, School of Engineering, Computing and Mathematics, Oxford Brookes University, UK.
Professor, Institut Supérieur de Gestion Tunis, Tunisia.
CNRS Researcher, Compiègne University of Technology, France.
Ronald G. Harper Distinguished Professor of Artificial Intelligence, School of Business University of Kansas, USA.
LinkedIn Group firstname.lastname@example.org