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522
The Transferable Belief Model
 ARTIFICIAL INTELLIGENCE
, 1994
"... We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions ..."
Abstract

Cited by 486 (15 self)
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We describe the transferable belief model, a model for representing quantified beliefs based on belief functions. Beliefs can be held at two levels: (1) a credal level where beliefs are entertained and quantified by belief functions, (2) a pignistic level where beliefs can be used to make decisions
The Generalized Pignistic Transformation
, 2004
"... This paper presents in detail the generalized pignistic transformation (GPT) succinctly developed in the DezertSmarandache Theory (DSmT) framework as a tool for decision process. The GPT allows to provide a subjective probability measure from any generalized basic belief assignment given by any c ..."
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Cited by 10 (2 self)
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This paper presents in detail the generalized pignistic transformation (GPT) succinctly developed in the DezertSmarandache Theory (DSmT) framework as a tool for decision process. The GPT allows to provide a subjective probability measure from any generalized basic belief assignment given by any
The Generalized Pignistic Transformation
, 2004
"... Abstract – This paper presents in detail the generalized pignistic transformation (GPT) succinctly developed in the DezertSmarandache Theory (DSmT) framework as a tool for decision process. The GPT allows to provide a subjective probability measure from any generalized basic belief assignment given ..."
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Abstract – This paper presents in detail the generalized pignistic transformation (GPT) succinctly developed in the DezertSmarandache Theory (DSmT) framework as a tool for decision process. The GPT allows to provide a subjective probability measure from any generalized basic belief assignment
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
A Generalization of the Pignistic Transform for Partial Bet
"... Abstract. The Transferable Belief Model is a powerful interpretation of belief function theory where decision making is based on the pignistic transform. Smets has proposed a generalization of the pignistic transform which appears to be equivalent to the Shapley value in the transferable utility mod ..."
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Cited by 2 (2 self)
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Abstract. The Transferable Belief Model is a powerful interpretation of belief function theory where decision making is based on the pignistic transform. Smets has proposed a generalization of the pignistic transform which appears to be equivalent to the Shapley value in the transferable utility
Consonant Belief Function induced by a Confidence Set of Pignistic Probabilities
"... 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 distributionPX of a random variable X is known, then the belief function quantif ..."
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Cited by 5 (0 self)
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quantifying our belief regarding a future realization of X should have its pignistic probability distribution equal toPX. WhenPX is unknown but a random sample of X is available, it is possible to build a set P of probability distributions containingPX with some confidence level. Following the Least
Two kadditive generalizations of the pignistic transform for imprecise decision making
"... The Transferable Belief approach to the Theory of Evidence is based on the pignistic transform which, mapping belief functions to probability distributions, allows to make “precise ” decisions on a set of disjoint hypotheses via classical utility theory. In certain scenarios, however, such as medica ..."
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Cited by 1 (0 self)
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The Transferable Belief approach to the Theory of Evidence is based on the pignistic transform which, mapping belief functions to probability distributions, allows to make “precise ” decisions on a set of disjoint hypotheses via classical utility theory. In certain scenarios, however
Possibility Theory as a Basis for Qualitative Decision Theory
, 1995
"... A counterpart to von Neumann and Morgenstern' expected utility theory is proposed in the framework of possibility theory. The existence of a utility function, representing a preference ordering among possibility distributions (on the consequences of decisionmaker's actions) that satisfies ..."
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Cited by 127 (29 self)
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A counterpart to von Neumann and Morgenstern' expected utility theory is proposed in the framework of possibility theory. The existence of a utility function, representing a preference ordering among possibility distributions (on the consequences of decisionmaker's actions
A Comparison of Methods for Transforming Belief Function Models to Probability Models
 IN T. D. NIELSEN &N.L.ZHANG (EDS.), SYMBOLIC
, 2003
"... Recently, we proposed a new method called the plausibility transformation method to convert a belief function model to an equivalent probability model. In this paper, we compare the plausibility transformation method with the pignistic transformation method. The two ..."
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Cited by 24 (1 self)
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Recently, we proposed a new method called the plausibility transformation method to convert a belief function model to an equivalent probability model. In this paper, we compare the plausibility transformation method with the pignistic transformation method. The two
Results 1  10
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522