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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 ..."
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Cited by 489 (16 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
Axioms for probability and belieffunction propagation
 Uncertainty in Artificial Intelligence
, 1990
"... In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We ..."
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Cited by 164 (23 self)
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marginals of joint probability distributions and joint belief functions fits the general framework. 1.
Belief Functions and the Transferable Belief
, 2000
"... Belief functions have been proposed for modeling someone’s degrees of belief. They provide alternatives to the models based on probability functions or on possibility functions. There are several interpretations of belief functions: the lower probabilities model, Dempster’s model, the hint model, th ..."
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Belief functions have been proposed for modeling someone’s degrees of belief. They provide alternatives to the models based on probability functions or on possibility functions. There are several interpretations of belief functions: the lower probabilities model, Dempster’s model, the hint model
Matrix Calculus for Belief Functions
, 2001
"... The mathematic of belief functions can be handled by the use of the matrix notation. This representation helps greatly the user thanks to its notational simplicity and its efficiency for proving theorems. We show how to use them for several problems related to belief functions and the transferable b ..."
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Cited by 28 (0 self)
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The mathematic of belief functions can be handled by the use of the matrix notation. This representation helps greatly the user thanks to its notational simplicity and its efficiency for proving theorems. We show how to use them for several problems related to belief functions and the transferable
Managing Decomposed Belief Functions
"... In this paper we develop a method for clustering all types of belief functions, in particular nonconsonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions ..."
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Cited by 6 (5 self)
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In this paper we develop a method for clustering all types of belief functions, in particular nonconsonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
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Cited by 101 (7 self)
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The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible
Belief Functions on Real Numbers.
, 2005
"... We generalize the TBM (transferable belief model) to the case where the frame of discernment is the extended set of real numbers R = [−∞, ∞], under the assumptions that ‘masses’ can only be given to intervals. Masses become densities, belief functions, plausibility functions and commonality function ..."
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Cited by 29 (0 self)
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We generalize the TBM (transferable belief model) to the case where the frame of discernment is the extended set of real numbers R = [−∞, ∞], under the assumptions that ‘masses’ can only be given to intervals. Masses become densities, belief functions, plausibility functions and commonality
On consistent belief functions
"... In this paper we define the class of consistent belief functions as the counterparts of consistent knowledge bases in classical logic. We prove that such class can be defined univocally no matter our definition of proposition implied by a belief function. As consistency can be desirable in decision ..."
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In this paper we define the class of consistent belief functions as the counterparts of consistent knowledge bases in classical logic. We prove that such class can be defined univocally no matter our definition of proposition implied by a belief function. As consistency can be desirable in decision
Geometric conditioning of belief functions
 in Proceedings of the First Workshop on the Theory of Belief Functions
, 2010
"... Abstract—In this paper we study the problem of conditioning a belief function b with respect to an event A by geometrically projecting such belief function onto the simplex associated with A in the simplex of all belief functions. Two different such simplices can be defined, as each belief function ..."
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Cited by 8 (7 self)
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Abstract—In this paper we study the problem of conditioning a belief function b with respect to an event A by geometrically projecting such belief function onto the simplex associated with A in the simplex of all belief functions. Two different such simplices can be defined, as each belief function
Results 1  10
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4,444