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Books on the topic 'Extended Dempster Shafer theory'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Wilson, Peter Nicholas. Some theoretical aspects of the Dempster-Shafer theory. Oxford Polytechnic, 1992.

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2

1941-, Yager Ronald R., Kacprzyk Janusz, and Fedrizzi Mario 1949-, eds. Advances in the Dempster-Shafer theory of evidence. Wiley, 1994.

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3

Ma, Yong. On the decision making problem in Dempster-Shafer theory. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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4

Yager, Roland R., and Liping Liu, eds. Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-44792-4.

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5

Liu, Liping, and Ronald R. Yager. Classic works of the Dempster-Shafer theory of belief functions. Springer, 2008.

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6

Kohlas, Jürg. A mathematical theory of hints: An approach to the Dempster-Shafer theory of evidence. Springer, 1995.

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7

Kramosil, Ivan. Probabilistic analysis of belief functions. Kluwer Academic/Plenum Publishers, 2001.

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8

Půlpán, Zdeněk. K problematice hledání podstatného v humanitních vědách. Academia, 2001.

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9

Limbourg, Philipp. Dependability modelling under uncertainty: An imprecise probabilistic approach. Springer, 2008.

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10

Florentin, Smarandache, and Dezert Jean, eds. Advances and applications of DSmT for information fusion: Collected works. American Research Press, 2004.

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11

United States. National Aeronautics and Space Administration., ed. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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12

Korvin, Andre De. Fuzzy sets, rough sets, and modeling evidence: Theory and application : a Dempster-Shafer based approach to compromise decision making with multiattributes applied to product selection. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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13

Kacprzyk, Janusz, Mario Fedrizzi, and Ronald R. Yager. Advances in the Dempster-Shafer Theory of Evidence. Wiley & Sons, Incorporated, John, 2008.

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14

Liu, Liping, and Ronald R. Yager. Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer, 2010.

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15

Kohlas, Juerg, and Paul-Andre Monney. Mathematical Theory of Hints: An Approach to the Dempster-Shafer Theory of Evidence. Springer London, Limited, 2013.

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16

Cleckner, William H. IV. Tactical evidential reasoning: an application of the Dempster-Shafer theory evidence. 1985.

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17

(Editor), Ronald Yager, and Liping Liu (Editor), eds. Classic Works of the Dempster-Shafer Theory of Belief Functions (Studies in Fuzziness and Soft Computing). Springer, 2007.

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18

Limbourg, Philipp. Dependability Modelling under Uncertainty: An Imprecise Probabilistic Approach. Springer Berlin / Heidelberg, 2010.

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19

Salicone, Simona. Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence. Springer, 2010.

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20

Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence (Springer Series in Reliability Engineering). Springer, 2006.

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21

Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence. Springer, 2007.

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22

Mock, Theodore J., and Rajendra P. Srivastava. Belief Functions in Business Decisions. Physica-Verlag, 2013.

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23

Mock, Theodore J., and Rajendra P. Srivastava. Belief Functions in Business Decisions. Physica, 2010.

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24

Advances and Applications of DSmT for Information Fusion (Collected works). Am. Res. Press, 2006.

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25

(Editor), Rajendra P. Srivastava, and Theodore J. Mock (Editor), eds. Belief Functions in Business Decisions (Studies in Fuzziness and Soft Computing). Physica-Verlag Heidelberg, 2002.

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26

Titelbaum, Michael G. Fundamentals of Bayesian Epistemology 2. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192863140.001.0001.

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This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a “credence”). It then presents Bayesians’ five core normative rules governing degrees of belief: Kolmogorov’s three probability axioms, the Ratio Formula for conditional credences, and Conditionalization for updating credences over time. After considering a few proposed additions to these norms, it applies the core rules to confirmation and decision theory. The book then details arguments for the Bayesian rules based on representation

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27

Titelbaum, Michael G. Fundamentals of Bayesian Epistemology 1. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198707608.001.0001.

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Abstract:

This book introduces readers to the fundamentals of Bayesian epistemology. It begins by motivating and explaining the idea of a degree of belief (also known as a “credence”). It then presents Bayesians’ five core normative rules governing degrees of belief: Kolmogorov’s three probability axioms, the Ratio Formula for conditional credences, and Conditionalization for updating credences over time. After considering a few proposed additions to these norms, it applies the core rules to confirmation and decision theory. The book then details arguments for the Bayesian rules based on representation

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