Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Assumptions, beliefs and probabilities
Artificial Intelligence
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision analysis using belief functions
International Journal of Approximate Reasoning
The dynamic of belief in the transferable belief model and specialization-generalization matrices
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Rejoinders to comments on “Perspectives on the theory and practice of belief functions”
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Artificial Intelligence
Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
What is Dempster-Shafer's model?
Advances in the Dempster-Shafer theory of evidence
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Handbook of defeasible reasoning and uncertainty management systems: Volume 4 abductive reasoning and learning
Probability of Deductibility and Belief Functions
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Decision-Making with Belief Functions and Pignistic Probabilities
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Cluster-based Specification Techniques in Dempster-Shafer Theory
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
The alpha-junctions: Combination Operators Applicable to Belief Functions
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Multisensor Data Fusion in Situation Assessment Processes
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Quantifying beliefs by belief functions: an axiomatic justification
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Characterizing belief with minimum commitment
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
The canonical decomposition of a weighted belief
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Coarsening Approximations of Belief Functions
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Combination of paradoxical sources of information within the neutrosophic framework
Proceedings of the first international conference on Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics
Editorial: Special issue in memory of Philippe Smets (1938--2005)
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
In Memoriam: Philippe Smets (1938--2005)
Fuzzy Sets and Systems
In memoriam: Philippe Smets (1938-2005)
International Journal of Approximate Reasoning
A belief function classifier based on information provided by noisy and dependent features
International Journal of Approximate Reasoning
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We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by 'missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the intersensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle 'messy' data problems