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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Consonant Belief Function Induced by a Confidence Set of Pignistic Probabilities
verfasst von : Astride Aregui, Thierry Denoeux
Erschienen in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Verlag: Springer Berlin Heidelberg
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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 ℙ
X
of 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 ℙ
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 ℙ
X
with 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.