Results 21 to 30 of about 203,140 (297)
Variable Selection Bias in Classification Trees Based on Imprecise Probabilities [PDF]
, 2005 Classification trees based on imprecise probabilities provide an advancement of classical classification trees. The Gini Index is the default splitting criterion in classical classification trees, while in classification trees based on imprecise ...
Carolin Strobl, Strobl, Carolin
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Reasoning with imprecise probabilities
International Journal of Approximate Reasoning, 2007 This special issue of the International Journal of Approximate Reasoning (IJAR) grew out of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA’05), held in Pittsburgh, USA, in July 2005 (http://www.sipta.org/isipta05). The symposium was organized by Teddy Seidenfeld, Robert Nau, and Fabio G.
Andrés Cano +2 more
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A nonparametric predictive alternative to the Imprecise Dirichlet Model: the case of a known number of categories [PDF]
, 2006 Nonparametric Predictive Inference (NPI) is a general methodology to learn from data in the absence of prior knowledge and without adding unjustified assumptions.
Augustin, Thomas +3 more
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Less is More: Decision Making & Information Sharing Under Severe Uncertainty
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 As the threat of misinformation grows in the digital age, so too grows the urgency to understand how evidence- sharing in communities impacts consensus-building on matters of fact.
Gene Lam, Arthur Paul Pedersen
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Imprecise probability trees: Bridging two theories of imprecise probability
Artificial Intelligence, 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 game-theoretic account of probability. We show that the two theories are more closely related than would be suspected at first sight, and we ...
Gert de Cooman, Filip Hermans
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Probabilistic-possibilistic belief networks [PDF]
, 2008 The interpretation of membership functions of fuzzy sets as statistical likelihood functions leads to a probabilistic-possibilistic hierarchical description of uncertain knowledge.
Cattaneo, Marco E. G. V.
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The aggregation of imprecise probabilities [PDF]
Journal of Statistical Planning and Inference, 2002 Imprecise probabilities are elicited from a group of experts in terms of betting rates. Two approaches to the aggregation of imprecise probabilities are presented. First, confidence-weighted lower and upper probabilities are used as the fundamental representation of uncertainty.
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Likelihood-based Imprecise Regression [PDF]
, 2011 We introduce a new approach to regression with imprecisely observed data, combining likelihood inference with ideas from imprecise probability theory, and thereby taking different kinds of uncertainty into account.
Marco E. G. V. Cattaneo +4 more
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Generalized basic probability assignments [PDF]
, 2002 Dempster-Shafer theory allows to construct belief functions from (precise) basic probability assignments. The present paper extends this idea substantially.
Augustin, Thomas
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Imprecise Bayesian Networks as Causal Models
Information, 2018 This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for
David Kinney
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