Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities

  • Authors:

  • Astride Aregui;Thierry Denœux

  • Affiliations:

  • HEUDIASYC, UTC, CNRS, Centre de Recherche de Royallieu, BP 20529, F-60205 Compiègne, France and CIRSEE, Suez Environnement, 38 rue du Pdt Wilson, F-78230 Le Pecq, France;HEUDIASYC, UTC, CNRS, Centre de Recherche de Royallieu, BP 20529, F-60205 Compiègne, France

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 P"X is unknown but a random sample of X is available, it is possible to build a set 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 P. Our method selects the most committed consonant belief function verifying this property. This general principle is applied to arbitrary discrete distributions as well as exponential and normal distributions. The efficiency of this approach is demonstrated using a simulated multi-sensor classification problem.