Instructor: Thierry Denoeux

Description: This is an introductory course on belief functions, with focus on data analysis, machine learning and statistical inference.

Slides

1. Lecture 1 - Representation and combination of evidence

Exercises

Lecture 1

Papers

1. T. Denoeux. Constructing Belief Functions from Sample Data Using Multinomial Confidence Regions. International Journal of Approximate Reasoning, Vol. 42, Issue 3, Pages 228-252, 2006. pdf
2. T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE transactions on Systems, Man and Cybernetics A, 30(2):131-150, 2000. pdf
3. T. Denoeux, S. Sriboonchitta and O. Kanjanatarakul. Evidential clustering of large dissimilarity data. Knowledge-Based Systems, vol. 106, pages 179-195, 2016. pdf
4. T. Denoeux. Maximum likelihood estimation from Uncertain Data in the Belief Function Framework. IEEE Transactions on Knowledge and Data Engineering, Vol. 25, Issue 1, pages 119-130, 2013. pdf
5. O. Kanjanatarakul, T. Denoeux and S. Sriboonchitta. Prediction of future observations using belief functions: a likelihood-based approach. International Journal of Approximate Reasoning, Vol. 72, pages 71-94, 2016. pdf

R code

1. Behrens-Fisher example