This half-day tutorial on Belief function (random sets) for the working scientist was presented on July 9th 2016 at the latest International Joint Conference on Artificial Intelligence (IJCAI-16).
The tutorial is very comprehensive (468 slides), covering: (i) a review of mathematical probability and its interpretations (Bayesian and frequentist); (ii) the rational for going beyond standard probability: it's all about the data! (iii) the basis notions of the theory of belief functions; (iv) reasoning with belief functions: inference, combination/conditioning, graphical models, decision making; (v) using belief functions for classification, regression, estimation, etc; (vi) dealing with computational issues and extending belief measures to real numbers; (vii) the main frameworks derived from belief theory, and its relationship with other theories of uncertainty; (viii) a number of example applications; (ix) new horizons, from the formulation of limit theorems for random sets, generalising the notion of likelihood and logistic regression for rare event estimation, climatic change modelling and new foundations for machine learning based on random set theory, a geometry of uncertainty. Tutorial slides are downloadable at http://cms.brookes.ac.uk/staff/FabioCuzzolin/files/IJCAI2016.pdf ------------------------------------------------------------------------------------- Dr Fabio Cuzzolin Reader Head of Artificial Intelligence and Vision Department of Computing and Communication Technologies Oxford Brookes University Oxford, UK http://cms.brookes.ac.uk/staff/FabioCuzzolin/ +44 (0)1865 484526
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