Call for papers

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5th International Conference on Belief Functions (BELIEF 2018)
9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018)

The Fifth International Conference on Belief Functions (BELIEF 2018) and the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018) will be held jointly in Compiègne, France, on September 17-21, 2018.

The BELIEF and SMPS conferences are biennial events concerning the modeling of uncertainty. The BELIEF conferences are sponsored by the Belief Functions and Applications Society (BFAS) and are focused on the theory of belief functions, while the scope of SMPS covers the application of all approaches to uncertainty (including fuzzy and rough sets, imprecise probabilities, etc.) to statistics and data analysis. The co-location of the two events is intended to favor cross-fertilization among researchers active in both communities.

The joint events welcomes submissions of both applied and theoretical papers dealing with soft methods in probability and statistics as well as papers devoted to the advance of belief function theory.

Typical topics of SMPS conferences include, but are not limited to:

  • Analysis of Censored Data
  • Analysis of Fuzzy Data
  • Random Sets
  • Fuzzy Random Variables
  • Fuzzy Regression Methods
  • Triangular Norms and Copulas
  • Imprecise Probabilities
  • Dempster-Shafer Theory
  • Robust Methods
  • Heuristic Optimization
  • Soft Computing and Statistics
  • Statistical Software for Imprecise Data

Typical topics of BELIEF conferences include, but are not limited to:

  • Decision making
  • Combination rules
  • Conditioning
  • Continuous belief functions
  • Independence and graphical models
  • Geometry and distance metrics
  • Mathematical foundations
  • Computational frameworks
  • Data and information fusion
  • Links with other uncertainty theories
  • Tracking and data association

Topics of interest to both conferences include, but are not limited to:

  • Statistical Inference
  • Machine Learning and Pattern recognition
  • Clustering and Classification
  • Graphical Models
  • Applications in network analysis
  • Applications in environment and climate change
  • Applications in biology and medical diagnosis
  • Applications in risk and reliability analysis
  • Applications in business and economics
  • Applications in vision and image processing