Artificial Intelligence
New Semantics for Quantitative Possibility Theory
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A definition of subjective possibility
International Journal of Approximate Reasoning
Inferring a possibility distribution from empirical data
Fuzzy Sets and Systems
Belief functions on real numbers
International Journal of Approximate Reasoning
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Practical uses of belief functions
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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A new method is proposed for building a predictive belief function from statistical data in the Transferable Belief Model framework. The starting point of this method is the assumption that, if the probability distribution 茂戮驴Xof a random variable X is known, then the belief function quantifying our belief regarding a future realization of X should have its pignistic probability distribution equal to 茂戮驴X. When PX is unknown but a random sample of X is available, it is possible to build a set $\mathcal{P}$ of probability distributions containing 茂戮驴Xwith some confidence level. Following the Least Commitment Principle, we then look for a belief function less committed than all belief functions with pignistic probability distribution in $\mathcal{P}$. Our method selects the most committed consonant belief function verifying this property. This general principle is applied to the case of the normal distribution.