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IMPRECISE PROBABILITY TREES: BRIDGING TWO THEORIES OF IMPRECISE PROBABILITY
, 2008
"... We give an overview of two approaches to probability theory where lower and upper probabilities, rather than probabilities, are used: Walley’s behavioural theory of imprecise probabilities, and Shafer and Vovk’s gametheoretic account of probability. We show that the two theories are more closely re ..."
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Cited by 17 (5 self)
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We give an overview of two approaches to probability theory where lower and upper probabilities, rather than probabilities, are used: Walley’s behavioural theory of imprecise probabilities, and Shafer and Vovk’s gametheoretic account of probability. We show that the two theories are more closely
Robust Classification for Imprecise Environments
, 1989
"... In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclas ..."
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Cited by 332 (15 self)
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In realworld environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We present a method for the comparison of classifier performance that is robust to imprecise class distributions
Imprecise Probability
, 2006
"... The term imprecise probability refers to a collection of theories which describe probabilities in an imprecise way. The following theories ordered in increasing generality belong to the concept of imprecise probability [8]. ..."
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The term imprecise probability refers to a collection of theories which describe probabilities in an imprecise way. The following theories ordered in increasing generality belong to the concept of imprecise probability [8].
Computation with imprecise probabilities
 in Proceedings of the 12th International Conference Information Processing and Management of Uncertainty in Knowledgebased Systems
, 2008
"... An imprecise probability distribution is an instance of secondorder uncertainty, that is, uncertainty about uncertainty, or uncertainty for short. Another instance is an imprecise possibility distribution. Computation with imprecise probabilities is not an academic exercise—it is a bridge to real ..."
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Cited by 1 (0 self)
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An imprecise probability distribution is an instance of secondorder uncertainty, that is, uncertainty about uncertainty, or uncertainty for short. Another instance is an imprecise possibility distribution. Computation with imprecise probabilities is not an academic exercise—it is a bridge
Algorithms for Imprecise Probabilities
"... This paper deals with algorithms for efficient calculations with imprecise probabilities. All of them are based on local computation techniques. The algorithms are classified according to the underlying independence assumptions (including the case of nonindependence). For each situation, the main c ..."
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Cited by 6 (1 self)
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This paper deals with algorithms for efficient calculations with imprecise probabilities. All of them are based on local computation techniques. The algorithms are classified according to the underlying independence assumptions (including the case of nonindependence). For each situation, the main
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions
 In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining
, 1997
"... Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust to imprecis ..."
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Cited by 306 (15 self)
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to imprecise class distributions and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management
On the Use of Imprecise Probabilities in Reliability
 Quality and Reliability Engineering International
, 2004
"... Theory of imprecise probability generalizes classical probability theory, by assigning to each event an interval instead of a single number. In this paper, we briefly discuss this generalization and some recently suggested applications of imprecise probabilities in reliability. We also comment on ..."
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Cited by 10 (5 self)
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Theory of imprecise probability generalizes classical probability theory, by assigning to each event an interval instead of a single number. In this paper, we briefly discuss this generalization and some recently suggested applications of imprecise probabilities in reliability. We also comment
1Decision Making with Imprecise Probabilities
"... Orthodox Bayesian decision theory requires an agent’s beliefs representable by a realvalued function, ideally a probability function. Many theorists have argued this is too restrictive; it can be perfectly reasonable to have indeterminate degrees of belief. So doxastic states are ideally representa ..."
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representable by a set of probability functions. One consequence of this is that the expected value of a gamble will be imprecise. This paper looks at the attempts to extend Bayesian decision theory to deal with such cases, and concludes that all proposals advanced thus far have been incoherent. A more modest
Results 1  10
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143,242