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Belief Functions: The Disjunctive Rule of Combination and the Generalized Bayesian Theorem
"... We generalize the Bayes ’ theorem within the transferable belief model framework. The Generalized Bayesian Theorem (GBT) allows us to compute the belief over a space Θ givenanobservationx⊆Xwhen one knows only the beliefs over X for every θi ∈ Θ. We also discuss the Disjunctive Rule of Combination ( ..."
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Cited by 171 (7 self)
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We generalize the Bayes ’ theorem within the transferable belief model framework. The Generalized Bayesian Theorem (GBT) allows us to compute the belief over a space Θ givenanobservationx⊆Xwhen one knows only the beliefs over X for every θi ∈ Θ. We also discuss the Disjunctive Rule of Combination (DRC) for distinct pieces of evidence. This rule allows us to compute the belief over X from the beliefs induced by two distinct pieces of evidence when one knows only that one of the pieces of evidence holds. The properties of the DRC and GBT and their uses for belief propagation in directed belief networks are analysed. The use of the discounting factors is justfied. The application of these rules is illustrated by an example of medical diagnosis.
Decision Making in the TBM: the Necessity of the Pignistic Transformation
, 2004
"... In the transferable belief model(TBM), pignistic probabilities are used for decision making. The nature of the pignistic transformation is justified by a linearity requirement. We justify the origin of this requirement showing it is not ad hoc but unavoidable provides one accepts expected utility th ..."
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Cited by 93 (1 self)
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In the transferable belief model(TBM), pignistic probabilities are used for decision making. The nature of the pignistic transformation is justified by a linearity requirement. We justify the origin of this requirement showing it is not ad hoc but unavoidable provides one accepts expected utility theory.
Possibility theory and statistical reasoning
 Computational Statistics & Data Analysis Vol
, 2006
"... Numerical possibility distributions can encode special convex families of probability measures. The connection between possibility theory and probability theory is potentially fruitful in the scope of statistical reasoning when uncertainty due to variability of observations should be distinguished f ..."
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Cited by 59 (4 self)
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Numerical possibility distributions can encode special convex families of probability measures. The connection between possibility theory and probability theory is potentially fruitful in the scope of statistical reasoning when uncertainty due to variability of observations should be distinguished from uncertainty due to incomplete information. This paper proposes an overview of numerical possibility theory. Its aim is to show that some notions in statistics are naturally interpreted in the language of this theory. First, probabilistic inequalites (like Chebychev’s) offer a natural setting for devising possibility distributions from poor probabilistic information. Moreover, likelihood functions obey the laws of possibility theory when no prior probability is available. Possibility distributions also generalize the notion of confidence or prediction intervals, shedding some light on the role of the mode of asymmetric probability densities in the derivation of maximally informative interval substitutes of probabilistic information. Finally, the simulation of fuzzy sets comes down to selecting a probabilistic representation of a possibility distribution, which coincides with the Shapley value of the corresponding consonant capacity. This selection process is in agreement with Laplace indifference principle and is closely connected with the mean interval of a fuzzy interval. It sheds light on the “defuzzification ” process in fuzzy set theory and provides a natural definition of a subjective possibility distribution that sticks to the Bayesian framework of exchangeable bets. Potential applications to risk assessment are pointed out. 1
Artificial Reasoning with Subjective Logic
, 1997
"... This paper defines a framework for artificial reasoning called Subjective Logic, which consists of a belief model called opinion and set of operations for combining opinions. Subjective Logic is an extension of standard logic that uses continuous uncertainty and belief parameters instead of only ..."
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Cited by 59 (14 self)
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This paper defines a framework for artificial reasoning called Subjective Logic, which consists of a belief model called opinion and set of operations for combining opinions. Subjective Logic is an extension of standard logic that uses continuous uncertainty and belief parameters instead of only discrete truth values. It can also be seen as an extension of classical probability calculus by using a second order probability representation instead of the standard first order representation. In addition to the standard logical operations, Subjective Logic contains some operations specific for belief theory such as consensus and recommendation. In particular, we show that Dempster's consensus rule is inconsistent with Bayes' rule and therefore is wrong, and provide an alternative rule with a solid mathematical basis. Subjective Logic is directly compatible with traditional mathematical frameworks, but is also suitable for handling ignorance and uncertainty which is required in artificial...
The Consensus Operator for Combining Beliefs
 ARTIFICIAL INTELLIGENCE JOURNAL
, 2002
"... The consensus operator provides a method for combining possibly conflicting beliefs within the DempsterShafer belief theory, and represents an alternative to the traditional Dempster 's rule. This paper describes how the consensus operator can be applied to dogmatic conflicting opinions, i.e. ..."
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Cited by 58 (19 self)
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The consensus operator provides a method for combining possibly conflicting beliefs within the DempsterShafer belief theory, and represents an alternative to the traditional Dempster 's rule. This paper describes how the consensus operator can be applied to dogmatic conflicting opinions, i.e. when the degree of conflict is very high. It overcomes shortcomings of Dempster's rule and other operators that have been proposed for combining possibly conflicting beliefs.
Data Fusion in the Transferable Belief Model.
, 2000
"... When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this socalled Dempster's rule of combination have been produced and the meaning of ..."
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Cited by 52 (0 self)
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When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this socalled Dempster's rule of combination have been produced and the meaning of distinctness has been assessed. We will present practical applications where the fusion of uncertain data is well achieved by Dempster's rule of combination. It is essential that the meaning of the belief functions used to represent uncertainty be well fixed, as the adequacy of the rule depends strongly on a correct understanding of the context in which they are applied. Missing to distinguish between the upper and lower probabilities theory and the transferable belief model can lead to serious confusion, as Dempster's rule of combination is central in the transferable belief model whereas it hardly fits with the upper and lower probabilities theory. Keywords: belief function, transferable beli...
Practical Representation of Incomplete Probabilistic Information
 Advances in Soft Computing:Soft Methods of Probability and Statistics conference
, 2004
"... This article deals with the compact representation of incomplete probabilistic knowledge which can be encountered in risk evaluation problems, for instance in environmental studies. Various kinds of knowledge are considered such as expert opinions about characteristics of distributions or poor stati ..."
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Cited by 51 (14 self)
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This article deals with the compact representation of incomplete probabilistic knowledge which can be encountered in risk evaluation problems, for instance in environmental studies. Various kinds of knowledge are considered such as expert opinions about characteristics of distributions or poor statistical information. Our approach is based on probability families encoded by possibility distributions and belief functions. In each case, a technique for representing the available imprecise probabilistic information faithfully is proposed, using different uncertainty frameworks (possibility theory, probability theory, belief functions...). Moreover the use of probabilitypossibility transformations enables confidence intervals to be encompassed by cuts of possibility distributions, thus making the representation stronger. The respective appropriateness of pairs of cumulative distributions, continuous possibility distributions or discrete random sets for representing information about the mean value, the mode, the median and other fractiles of illknown probability distributions is discussed in detail.
ON THE PLAUSIBILITY TRANSFORMATION METHOD FOR TRANSLATING BELIEF FUNCTION MODELS TO PROBABILITY MODELS
, 2006
"... In this paper, we propose the plausibility transformation method for translating DempsterShafer (DS) belief function models to probability models, and describe some of its properties. There are many other transformation methods used in the literature for translating belief function models to proba ..."
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Cited by 47 (1 self)
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In this paper, we propose the plausibility transformation method for translating DempsterShafer (DS) belief function models to probability models, and describe some of its properties. There are many other transformation methods used in the literature for translating belief function models to probability models. We argue that the plausibility transformation method produces probability models that are consistent with DS semantics of belief function models, and that, in some examples, the pignistic transformation method produces results that appear to be inconsistent with Dempster’s rule of combination.
The DempsterShafer calculus for statisticians
 International Journal of Approximate Reasoning
, 2007
"... The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “ ..."
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Cited by 46 (1 self)
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The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “don’t know”. Arguments are presented for the necessity of “don’t know”. Elements of the calculus are sketched, including the extension of a DS model from a margin to a full state space, and DS combination of independent DS uncertainty assessments on the full space. The methodology is applied to inference and prediction from Poisson counts, including an introduction to the use of jointree model structure to simplify and shorten computation. The relation of DS theory to statistical significance testing is elaborated, introducing along the way the new concept of “dull ” null hypothesis. Key words: DempsterShafer; belief functions; state space; Poisson model; jointree computation; statistical significance; dull null hypothesis 1