The present bibliography on the theory and applications of belief functions is maintained by Fabio Cuzzolin.
Everybody is very welcome to contribute to it by reporting title and details of relevant papers that cannot yet be found in this list!
Authors in alphabetical list. Last update: June 18th 2010.

All publications are tentatively classified into the following, broad categories:

• Foundations, Frameworks, Combination, Random sets, Geometry, Applications, Decision, Machine learning, TBM, Logic, Approximation, Possibility, Fuzzy, Algorithms, Conditioning, Independence

which are used to label the papers between brackets: e.g., [Foundations]. Download BibTeX version

1. S. Abel The sum-and-lattice points method based on an evidential reasoning system applied to the real-time vehicle guidance problem Uncertainty in Artificial Intelligence 2 (Lemmer and Kanal, eds.), 1988, pp. 365-370. [Applications]

2. J. Aitchinson Discussion on professor Dempster's paper Journal of the Royal Statistical Society B 30 (1968), 234-237. [Foundations]

3. R. Almond Belief function models for simple series and parallel systems Department of Statistics, University of Washington, Tech. Report 207 (1991).

4. R. G. Almond Fusion and propagation of graphical belief models: an implementation and an example PhD dissertation, Department of Statistics, Harvard University, 1990.

5. R. G. Almond Graphical belief modeling Chapman and Hall/CRC, 1995. [Graphical models]

6. P. An and W. M. Moon An evidential reasoning structure for integrating geophysical, geological and remote sensing data Proceedings of IEEE, 1993, pp. 1359-1361. [Applications]

7. Z. An Relative evidential support PhD dissertation, University of Ulster, 1991. [Foundations]

8. Z. An, D. A. Bell, and J. G. Hughes Relation-based evidential reasoning International Journal of Approximate Reasoning 8 (1993), 231-251. [Frameworks]

9. K. A. Andersen and J. N. Hooker A linear programming framework for logics of uncertainty Decision Support Systems 16 (1996), 39-53. [Logic]

10. Thomas Augustin Modeling weak information with generalized basic probability assignments Data Analysis and Information Systems - Statistical and Conceptual Approaches (H. H. Bock and W. Polasek, eds.), Springer, 1996, pp. 101-113.

11. A. Ayoun and Philippe Smets Data association in multi-target detection using the transferable belief model Intern. J. Intell. Systems (2001). [Applications,TBM]

12. J. F. Baldwin Evidential support logical programming Fuzzy Sets and Systems 24 (1985), 1-26. [Logic]

13. J. F. Baldwin Towards a general theory of evidential reasoning Proceedings of IPMU'90 (B. Bouchon-Meunier, R.R. Yager, and L.A. Zadeh, eds.), Paris, France, 2-6 July 1990, pp. 360-369. [Foundations]

14. J. F. Baldwin Combining evidences for evidential reasoning International Journal of Intelligent Systems Vol. 6, No. 6 (September 1991), 569-616. [Combination]

15. J. A. Barnett Computational methods for a mathematical theory of evidence Proc. of the 7th National Conference on Artificial Intelligence (AAAI-88), 1981, pp. 868-875. [Algorithms]

16. P. Baroni Extending consonant approximations to capacities Proceedings of IPMU'04, 2004, pp. 1127-1134. [Possibility,approximation]

17. M. Bauer A Dempster-Shafer approach to modeling agent preferences for plan recognition User Modeling and User-Adapted Interaction Vol. 5, No. 3-4 (1995), 317-348. [Applications]

18. M. Bauer Approximation algorithms and decision making in the Dempster-Shafer theory of evidence - An empirical study International Journal of Approximate Reasoning 17 (1997), 217-237. [Decision,approximation]

19. M. Bauer Approximations for decision making in the Dempster-Shafer theory of evidence Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (F. Horvitz, E.; Jensen, ed.), Portland, OR, USA, 1-4 August 1996, pp. 73-80. [Decision,approximation]

20. D. A. Bell and J. W. Guan Discounting and combination operations in evidential reasoning Uncertainty in Artificial Intelligence. Proceedings of the Ninth Conference (1993) (A. Heckerman, D.; Mamdani, ed.), Washington, DC, USA, 9-11 July 1993, pp. 477-484. [Combination]

21. D. A. Bell, J. W. Guan, and G. M. Shapcott Using the Dempster-Shafer orthogonal sum for reasoning which involves space Kybernetes 27:5 (1998), 511-526. [Combination]

22. D. A. Bell, J. W. Guan, and Suk Kyoon Lee Generalized union and project operations for pooling uncertain and imprecise information Data and Knowledge Engineering 18 (1996), 89-117.

23. A. Bendjebbour and W. Pieczynski Unsupervised image segmentation using Dempster-Shafer fusion in a Markov fields context Proceedings of the International Conference on Multisource-Multisensor Information Fusion (FUSION'98) (R. Hamid, A. Zhu, and D. Zhu, eds.), vol. 2, Las Vegas, NV, USA, 6-9 July 1998, pp. 595-600. [Applications]

24. S. Benferhat, Alessandro Saffiotti, and Philippe Smets Belief functions and default reasoning Proc. of the 11th Conf. on Uncertainty in AI, Montreal, Canada, 1995, pp. 19-26. [Logic]

25. S. Benferhat, Alessandro Saffiotti, and Philippe Smets Belief functions and default reasonings Tech. report, Universite' Libre de Bruxelles, Technical Report TR/IRIDIA/95-5, 1995. [Logic]

26. R. J. Beran On distribution-free statistical inference with upper and lower probabilities Annals of Mathematical Statistics 42 (1971), 157-168. [Foundations,upper-lower]

27. Berger Robust bayesian analysis: Sensitivity to the prior Journal of Statistical Planning and Inference 25 (1990), 303-328.

28. Ulla Bergsten and Johan Schubert Dempster's rule for evidence ordered in a complete directed acyclic graph International Journal of Approximate Reasoning 9 (1993), 37-73. [Combination,graphical models]

29. Ulla Bergsten, Johan Schubert, and P. Svensson Applying data mining and machine learning techniques to submarine intelligence analysis Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD'97) (D. Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, eds.), Newport Beach, USA, 14-17 August 1997, pp. 127-130. [Applications,machine learning]

30. P. Besnard and Jurg Kohlas Evidence theory based on general consequence relations Int. J. of Foundations of Computer Science 6 (1995), no. 2, 119-135. [Foundations]

31. B. Besserer, S. Estable, and B. Ulmer Multiple knowledge sources and evidential reasoning for shape recognition Proceedings of IEEE, 1993, pp. 624-631 [Applications]

32. Malcolm Beynon, Bruce Curry, and Peter Morgan The Dempster-Shafer theory of evidence: approach to multicriteria decision modeling OMEGA: The International Journal of Management Science 28 (2000), 37-50. [Decision]

33. Elisabetta Binaghi, L. Luzi, P. Madella, F. Pergalani, and A. Rampini Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches Natural Hazards 17 (1998), 77-97. [Applications]

34. Elisabetta Binaghi, Paolo Madella, I. Gallo, and A. Rampini A neural refinement strategy for a fuzzy Dempster-Shafer classifier of multisource remote sensing images Proceedings of the SPIE - Image and Signal Processing for Remote Sensing IV, vol. 3500, Barcelona, Spain, 21-23 Sept. 1998, pp. 214-224. [Applications,fuzzy]

35. Elisabetta Binaghi and Paolo Madella Fuzzy Dempster-Shafer reasoning for rule-based classifiers International Journal of Intelligent Systems 14 (1999), 559-583. [Fuzzy,machine learning]

36. R. Bissig, Jurg Kohlas, and N. Lehmann Fast-division architecture for Dempster-Shafer belief functions Qualitative and Quantitative Practical Reasoning, First International Joint Conference on Qualitative and Quantitative Practical Reasoning; ECSQARU-FAPR'97 (D. Gabbay, R. Kruse, A. Nonnengart, and H.J. Ohlbach, eds.), Springer, 1997. [Algorithms]

37. G. Biswas and T. S. Anand Using the Dempster-Shafer scheme in a mixed-initiative expert system shell Uncertainty in Artificial Intelligence, volume 3 (L.N. Kanal, T.S. Levitt, and J.F. Lemmer, eds.), North-Holland, 1989, pp. 223-239. [Applications]

38. P. Black Is Shafer general Bayes? Proceedings of the Third AAAI Uncertainty in Artificial Intelligence Workshop, 1987, pp. 2-9. [Foundations]

39. P. Black An examination of belief functions and other monotone capacities PhD dissertation, Department of Statistics, Carnegie Mellon University, 1996, Pgh. PA 15213. [Foundations]

40. P. Black Geometric structure of lower probabilities Random Sets: Theory and Applications (Goutsias, Malher, and Nguyen, eds.), Springer, 1997, pp. 361-383. [Geometry]

41. Isabelle Bloch Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account Pattern Recognition Letters 17 (1996), 905-919. [Applications,vision]

42. H. Borotschnig, L. Paletta, M. Prantl, and A. Pinz A comparison of probabilistic, possibilistic and evidence theoretic fusion schemes for active object recognition Computing 62 (1999), 293-319. [Possibility,applications,vision]

43. Michael Boshra and Hong Zhang Accommodating uncertainty in pixel-based verification of 3-D object hypotheses Pattern Recognition Letters 20 (1999), 689-698. [Applications,vision]

44. E. Bosse and J. Roy Fusion of identity declarations from dissimilar sources using the Dempster-Shafer theory Optical Engineering 36:3 (March 1997), 648-657. [Fusion]

45. J. R. Boston A signal detection system based on Dempster-Shafer theory and comparison to fuzzy detection IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 30:1 (February 2000), 45-51. [Fuzzy,applications]

46. L. Boucher, T. Simons, and P. Green Evidential reasoning and the combination of knowledge and statistical techniques in syllable based speech recognition Proceedings of the NATO Advanced Study Institute, Speech Recognition and Understanding. Recent Advances, Trends and Applications (R. Laface, P.; De Mori, ed.), Cetraro, Italy, 1-13 July 1990, pp. 487-492. [Applications]

47. M. Bruning and Dieter Denneberg Max-min sigma-additive representation of monotone measures Statistical Papers 34 (2002), 23-35. [Combinatorics]

48. Noel Bryson and Ayodele Mobolurin Qualitative discriminant approach for generating quantitative belief functions IEEE Transactions on Knowledge and Data Engineering 10 (1998), 345-348. [Inference]

49. Dennis M. Buede and Paul Girardi Target identification comparison of Bayesian and Dempster-Shafer multisensor fusion IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 27 (1997), 569-577. [Fusion,applications]

50. A. Bundy Incidence calculus: A mechanism for probability reasoning Journal of automated reasoning 1 (1985), 263-283. [Frameworks]

51. A. C. Butler, F. Sadeghi, S. S. Rao, and S. R. LeClair Computer-aided design/engineering of bearing systems using the Dempster-Shafer theory Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9(1) (1995), 1-11. [Applications]

52. R. Buxton Modelling uncertainty in expert systems International Journal of Man-Machine Studies 31 (1989), 415-476.

53. C. Camerer and M. Weber Recent developments in modeling preferences: uncertainty and ambiguity Journal of Risk and Uncertainty 5 (1992), 325-370.

54. F. Campos and F. M. C. de Souza Extending Dempster-Shafer theory to overcome counter intuitive results Proceedings of IEEE NLP-KE '05, vol. 3, 2005, pp. 729- 734. [Foundations]

55. J. Cano, M. Delgado, and Serafin Moral An axiomatic framework for propagating uncertainty in directed acyclic networks International Journal of Approximate Reasoning 8 (1993), 253-280. [Graphical models]

56. J. Carlson and R. R. Murphy Use of Dempster-Shafer conflict metric to adapt sensor allocation to unknown environments Tech. report, Safety Security Rescue Research Center, University of South Florida, 2005. [Applications]

57. Lucas Caro and Araabi Babak Nadjar Generalization of the Dempster-Shafer theory: a fuzzy-valued measure IEEE Transactions on Fuzzy Systems 7 (1999), 255-270. [Fuzzy]

58. W. F. Caselton and W. Luo Decision making with imprecise probabilities: Dempster-Shafer theory and application Water Resources Research 28 (1992), 3071-3083. [Decision]

59. Marco E. G. V. Cattaneo Combining belief functions issued from dependent sources Proc. of ISIPTA, 2003, pp. 133-147. [Combination,independence]

60. A. Chateauneuf and J. Y. Jaffray Some characterizations of lower probabilities and other monotone capacities through the use of Moebius inversion Mathematical Social Sciences 17 (1989), 263-283. [Combinatorics,geometry]

61. A. Chateauneuf and J.-C. Vergnaud Ambiguity reduction through new statistical data International Journal of Approximate Reasoning 24 (2000), 283-299. [Inference]

62. C. W. R. Chau, P. Lingras, and S. K. M. Wong Upper and lower entropies of belief functions using compatible probability functions Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems (ISMIS'93) (Z.W. Komorowski, J.; Ras, ed.), Trondheim, Norway, 15-18 June 1993, pp. 306-315. [Upper-lower]

63. A. Cheaito, M. Lecours, and E. Bosse A non-ad-hoc decision rule for the Dempster-Shafer method of evidential reasoning Proceedings of the SPIE - Sensor Fusion: Architectures, Algorithms, and Applications II, Orlando, FL, USA, 16-17 April 1998, pp. 44-57. [Decision]

64. A. Cheaito, M. Lecours, and E. Bosse Study of a modified Dempster-Shafer approach using an expected utility interval decision rule Proceedings of the SPIE - Sensor Fusion: Architectures, Algorithms, and Applications III, vol. 3719, Orlando, FL, USA, 7-9 April 1999, pp. 34-42. [Decision,utility]

65. Shiuh-Yung Chen,Wei-Chung Lin, and Chin-Tu Chen Spatial reasoning based on multivariate belief functions Proceedings of IEEE, 1992, pp. 624-626.

66. Shiuh-Yung Chen,Wei-Chung Lin, and Chin-Tu Chen Evidential reasoning based on Dempster-Shafer theory and its application to medical image analysis Proceedings of SPIE - Neural and Stochastic Methods in Image and Signal Processing II, vol. 2032, San Diego, CA, USA, 12-13 July 1993, pp. 35-46. [Applications,vision]

67. Y. Y. Chen Statistical inference based on the possibility and belief measures Transactions of the American Mathematical Society 347 (1995), 1855-1863. [Possibility,inference]

68. M. Clarke and Nic Wilson Efficient algorithms for belief functions based on the relationship between belief and probability Proceedings of the European Conference on Symbolic and Quantitative Approaches to Uncertaintyv (P. Kruse, R.; Siegel, ed.), Marseille, France, 15-17 October 1991, pp. 48-52. [Algorithms,approximation]

69. J. Van Cleynenbreugel, S. A. Osinga, F. Fierens, P. Suetens, and A. Oosterlinck Road extraction from multitemporal satellite images by an evidential reasoning approach Pattern Recognition Letters 12:6 (June 1991), 371-380. [Applications,vision]

70. B. R. Cobb and Prakash P. Shenoy On the plausibility transformation method for translating belief function models to probability models Int. J. Approx. Reasoning 41 (2006), no. 3, 314-330. [Approximation]

71. B. R. Cobb and Prakash P. Shenoy A comparison of methods for transforming belief function models to probability models Proceedings of ECSQARU'2003, Aalborg, Denmark, July 2003, pp. 255-266. [Approximation]

72. B. R. Cobb and Prakash P. Shenoy A comparison of Bayesian and belief function reasoning Information Systems Frontiers 5 (2003), no. 4, 345-358. [Approximation]

73. B. R. Cobb and Prakash P. Shenoy On transforming belief function models to probability models Tech. report, University of Kansas, School of Business, Working Paper No. 293, February 2003. [Approximation]

74. K. Coombs, D. Freel, and D. Lampert S. Brahm Using Dempster-Shafer methods for object classification in the theater ballistic missile environment Proceedings of the SPIE - Sensor Fusion: Architectures, Algorithms, and Applications III, vol. 3719, Orlando, FL, USA, 7-9 April 1999, pp. 103-113. [Applications]

75. Fabio G. Cozman Calculation of posterior bounds given convex sets of prior probability measures and likelihood functions Journal of Computational and Graphical Statistics 8(4) (1999), 824-838. [Credal sets]

76. Fabio G. Cozman and Serafin Moral Reasoning with imprecise probabilities International Journal of Approximate Reasoning 24 (2000), 121-123. [Foundations,frameworks]

77. H. H. Crapo and Gian-Carlo Rota On the foundations of combinatorial theory: combinatorial geometries M.I.T. Press, Cambridge, Mass., 1970. [Combinatorics]

78. Valerie Cross and Thomas Sudkamp Compatibility measures for fuzzy evidential reasoning Proceedings of the Fourth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Kauai, HI, USA, 2-5 June 1991, pp. 72-78. [Fuzzy]

79. Valerie Cross and Thomas Sudkamp Compatibility and aggregation in fuzzy evidential reasoning Proceedings of IEEE, 1991, pp. 1901-1906. [Fuzzy,combination]

80. Fabio Cuzzolin The geometry of relative plausibilities Proceedings of the 11th International Conference on Information Processing and Management of Uncertainty IPMU'06, special session on \Fuzzy measures and integrals, capacities and games. [Geometry,approximation]

81. Fabio Cuzzolin On the properties of relative plausibilities Proceedings of the International Conference of the IEEE Systems, Man, and Cybernetics Society (SMC'05), Hawaii, USA. [Geometry,approximation]

82. Fabio Cuzzolin Lattice modularity and linear independence 18th British Combinatorial Conference, Brighton, UK, 2001. [Independence,combinatorics]

83. Fabio Cuzzolin Visions of a generalized probability theory PhD dissertation, Universitą di Padova, Dipartimento di Elettronica e Informatica, 19 February 2001.

84. Fabio Cuzzolin Geometry of Dempster's rule of combination IEEE Transactions on Systems, Man and Cybernetics part B 34 (2004), no. 2, 961-977. [Combination,geometry]

85. Fabio Cuzzolin Simplicial complexes of finite fuzzy sets Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty IPMU'04, Perugia, Italy, 2004, pp. 1733-1740. [geometry,fuzzy]

86. Fabio Cuzzolin Algebraic structure of the families of compatible frames of discernment Annals of Mathematics and Artificial Intelligence 45(1-2) (2005), 241-274. [Combinatorics]

87. Fabio Cuzzolin Two new Bayesian approximations of belief functions based on convex geometry IEEE Trans. on Systems, Man, and Cybernetics - part B Vol. 37, No. 4, pp. 993-1008 (August 2007). [Geometry,approximation]

88. Fabio Cuzzolin Geometry of relative plausibility and relative belief of singletons Annals of Mathematics and Artificial Intelligence (2010) [Geometry,approximation]

89. Fabio Cuzzolin Dual properties of the relative belief of singletons "PRICAI 2008: Trends in Artificial Intelligence", Lecture Notes in Computer Science, Volume 5351/2008, pages 78-90 (2009). [approximation]

90. Fabio Cuzzolin A geometric approach to the theory of evidence IEEE Transactions on Systems, Man, and Cybernetics - Part C 38 (2008), no. 4, 522-534. [Geometry,frameworks]

91. Fabio Cuzzolin Three alternative combinatorial formulations of the theory of evidence Intelligent Decision Analysis journal (2010). [Foundations,combinatorics]

92. Fabio Cuzzolin On the credal structure of consistent probabilities "Logics in Artificial Intelligence", Lecture Notes in Computer Science, Volume 5293/2008, pages 126-139 (2008). [Geometry,credal sets]

93. Fabio Cuzzolin Semantics of the relative belief of singletons International Workshop on Uncertainty and Logic UNCLOG'08, Kanazawa, Japan, 2008. [Approximation]

94. Fabio Cuzzolin Complexes of outer consonant approximations Proceedings of ECSQARU'09, 2009. [Geometry,approximation]

95. Fabio Cuzzolin The geometry of consonant belief functions: simplicial complexes of necessity measures Fuzzy Sets and Systems (2010). [Geometry,possibility]

96. Fabio Cuzzolin Credal semantics of Bayesian transformations in terms of probability intervals IEEE Transactions on Systems, Man, and Cybernetics - Part B (2010). [Geometry,approximation]

97. Fabio Cuzzolin Geometry of upper probabilities Proceedings of the 3rd International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'03), July 2003. [Geometry,upper-lower]

98. Fabio Cuzzolin Geometry of Dempster's rule Proceedings of FSDK'02, Singapore, 18-22 November 2002. [Geometry,combination]

99. Fabio Cuzzolin and Ruggero Frezza Evidential modeling for pose estimation Proceedings of the 4rd Internation Symposium on Imprecise Probabilities and Their Applications (ISIPTA'05), Pittsburgh, July 2005. [Applications,vision]

100. Fabio Cuzzolin Families of compatible frames of discernment as semimodular lattices Proc. of the International Conference of the Royal Statistical Society (RSS2000), September 2000. [Combinatorics]

101. Fabio Cuzzolin and Ruggero Frezza An evidential reasoning framework for object tracking SPIE - Photonics East 99 - Telemanipulator and Telepresence Technologies VI (Matthew R. Stein, ed.), vol. 3840, 19-22 September 1999, pp. 13-24. [Applications,vision]

102. Fabio Cuzzolin and Ruggero Frezza Integrating feature spaces for object tracking Proc. of the International Symposium on the Mathematical Theory of Networks and Systems (MTNS2000), 21-25 June 2000. [Applications,vision]

103. Fabio Cuzzolin and Ruggero Frezza Geometric analysis of belief space and conditional subspaces Proceedings of the 2nd International Symposium on Imprecise Probabilities and their Applications (ISIPTA2001), 26-29 June 2001. [Conditioning,geometry,frameworks]

104. Fabio Cuzzolin and Ruggero Frezza Lattice structure of the families of compatible frames Proceedings of the 2nd International Symposium on Imprecise Probabilities and their Applications (ISIPTA2001), 26-29 June 2001. [Combinatorics]

105. Wagner Texeira da Silva and Ruy Luiz Milidiu Algorithms for combining belief functions International Journal of Approximate Reasoning 7 (1992), 73-94. [Combination,algorithms]

106. Milan Daniel On transformations of belief functions to probabilities International Journal of Intelligent Systems, special issue on Uncertainty Processing. [Approximation]

107. Milan Daniel Transformations of belief functions to probabilities Tech. report, Institute of Computer Science, Academy of Sciences of the Csech Republic. [Approximation]

108. Milan Daniel Consistency of probabilistic transformations of belief functions Proceedings of IPMU, 2004, pp. 1135-1142. [Approximation]

109. Milan Daniel Algebraic structures related to Dempster-Shafer theory Proceedings of the 5th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'94) (B. Bouchon-Meunier, R.R. Yager, and L.A. Zadeh, eds.), Paris, France, 4-8 July 1994, pp. 51-61. [Combinatorics]

110. L. de Campos, J. Huete, and Serafin Moral Probability intervals: a tool for uncertain reasoning Int. J. Uncertainty Fuzziness Knowledge-Based Syst. 1 (1994), 167-196. [Intervals,frameworks]

111. Gert de Cooman and D. Aeyels A random set description of a possibility measure and its natural extension (1998) [Possibility,random sets]

112. Gert de Cooman and Marco Zaffalon Updating beliefs with incomplete observations Artif. Intell. 159 (2004), no. 1-2, 75-125. [Combination]

113. J. Kamp¶e de F¶eriet Interpretation of membership functions of fuzzy sets in terms of plausibility and belief Fuzzy Information and Decision Processes (M. M. Gupta and E. Sanchez, eds.), North-Holland, Amsterdam, 1982, pp. 93-98. [Foundations,fuzzy]

114. F. Dupin de Saint Cyr, J. Lang, and N. Schiex Penalty logic and its link with Dempster-Shafer theory Proceedings of UAI'94, 1994, pp. 204-211. [Logic]

115. S. Demotier, W. Schon, and Thierry Denoeux Risk assessment based on weak information using belief functions: a case study in water treatment IEEE Transactions on Systems, Man and Cybernetics, Part C 36(3) (May 2006), 382-396. [Applications]

116. Arthur P. Dempster New methods for reasoning towards posterior distributions based on sample data Annals of Mathematical Statistics 37 (1966), 355-374.

117. Arthur P. Dempster Upper and lower probability inferences based on a sample from a finite univariate population Biometrika 54 (1967), 515-528.

118. Arthur P. Dempster Bayes, Fischer and belief functions Bayesian and Likelihood Methods in Statistics and Economics (S. J. Press S. Geisser, J. S. Hodges and A. Zellner, eds.), 1990.

119. Arthur P. Dempster Construction and local computation aspects of network belief functions Influence Diagrams, Belief Nets and Decision Analysis (R. M. Oliver and J. Q. Smith, eds.), Wiley, Chirichester, 1990.

120. Arthur P. Dempster Normal belief functions and the Kalman filter Tech. report, Department of Statistics, Harvard Univerisity, Cambridge, MA, 1990.

121. Arthur P. Dempster Upper and lower probabilities induced by a multivariate mapping Annals of Mathematical Statistics 38 (1967), 325-339.

122. Arthur P. Dempster A generalization of Bayesian inference Journal of the Royal Statistical Society, Series B 30 (1968), 205-247.

123. Arthur P. Dempster Upper and lower probabilities generated by a random closed interval Annals of Mathematical Statistics 39 (1968), 957-966.

124. Arthur P. Dempster Upper and lower probabilities inferences for families of hypothesis with monotone density ratios Annals of Mathematical Statistics 40 (1969), 953-969.

125. Arthur P. Dempster Lindley's paradox: Comment Journal of the American Statistical Association 77:378 (June 1982), 339-341.

126. Arthur P. Dempster and Augustine Kong Uncertain evidence and artificial analysis Tech. report, S-108, Department of Statistics, Harvard University, 1986.

127. C. Van den Acker Belief function representation of statistical audit evidence International Journal of Intelligent Systems 15 (2000), 277-290.

128. Dieter Denneberg Totally monotone core and products of monotone measures International Journal of Approximate Reasoning 24 (2000), 273-281.

129. Dieter Denneberg and Michel Grabisch Interaction transform of set functions over a finite set Information Sciences 121 (1999), 149-170.

130. Thierry Denoeux Inner and outer approximation of belief structures using a hierarchical clustering approach Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(4) (2001), 437-460. [Approximation]

131. Thierry Denoeux Construction of predictive belief functions using a frequentist approach IPMU, 2006.

132. Thierry Denoeux Conjunctive and disjunctive combination of belief functions induced by non distinct bodies of evidence Artificial Intelligence (2007). [Combination]

133. Thierry Denoeux A new justification of the unnormalized Dempsters rule of combination from the Least Commitment Principle Proceedings of FLAIRS'08, Special Track on Uncertaint Reasoning, 2008. [Combination]

134. Thierry Denoeux and A. Ben Yaghlane Approximating the combination of belief functions using the fast Moebius transform in a coarsened frame International Journal of Approximate Reasoning 31(1-2) (October 2002), 77-101. [Combination]

135. Thierry Denoeux Modeling vague beliefs using fuzzy-valued belief structures, Fuzzy Sets and Systems.

136. Thierry Denoeux A k-nearest neighbour classification rule based on Dempster-Shafer theory IEEE Transactions on Systems, Man, and Cybernetics 25:5 (1995), 804-813.

137. Thierry Denoeux Analysis of evidence-theoretic decision rules for pattern classification Pattern Recognition 30:7 (1997), 1095-1107. [Machine learning,decision]

138. Thierry Denoeux Reasoning with imprecise belief structures International Journal of Approximate Reasoning 20 (1999), 79-111.

139. Thierry Denoeux Allowing imprecision in belief representation using fuzzy-valued belief structures Proceedings of IPMU'98, vol. 1, July Paris, 1998, pp. 48-55.

140. Thierry Denoeux An evidence-theoretic neural network classifier Proceedings of the 1995 IEEE International Conference on Systems, Man, and Cybernetics (SMC'95), vol. 3, October 1995, pp. 712-717.

141. Thierry Denoeux and G. Govaert Combined supervised and unsupervised learning for system diagnosis using Dempster-Shafer theory Proceedings of the International Conference on Computational Engineering in Systems Applications, Symposium on Control, Optimization and Supervision, CESA '96 IMACS Multiconference, vol. 1, Lille, France, 9-12 July 1996, pp. 104-109.

142. Thierry Denouex Inner and outer approximation of belief structures using a hierarchical clustering approach International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(4) (2001), 437-460. [Approximation]

143. M. C. Desmarais and J. Liu Experimental results on user knowledge assessment with an evidential reasoning methodology Proceedings of the 1993 International Workshop on Intelligent User Interfaces (W.D. Gray, W.E. He°ey, and D. Murray, eds.), Orlando, FL, USA, 4-7 January 1993, pp. 223-225.

144. M. Deutsch-Mccleish A model for non-monotonic reasoning using Dempster's rule Uncertainty in Artificial Intelligence 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, and J.F. Lemmer, eds.), Elsevier Science Publishers, 1991, pp. 481-494.

145. M. Deutsch-McLeish A study of probabilities and belief functions under conflicting evidence: comparisons and new method Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'90) (B. Bouchon-Meunier, R.R. Yager, and L.A. Zadeh, eds.), Paris, France, 2-6 July 1990, pp. 41-49.

146. . M. Deutsch-McLeish, P. Yao, Fei Song, and T. Stirtzinger Knowledge-acquisition methods for finding belief functions with an application to medical decision making Proceedings of the International Symposium on Artificial Intelligence (H. Cantu-Ortiz, F.J.; Terashima-Marin, ed.), Cancun, Mexico, 13-15 November 1991, pp. 231-237. [Decision,applications]

147. Jean Dezert and F. Smarandache A new probabilistic transformation of belief mass assignment (2007).

148. P. Diaconis Review of 'a mathematical theory of evidence' Journal of American Statistical Society 73:363 (1978), 677-678.

149. A. F. Dragoni, P. Giorgini, and A. Bolognini Distributed knowledge elicitation through the Dempster-Shafer theory of evidence: a simulation study Proceedings of the Second International Conference on Multi-Agent Systems (ICMAS'96), Kyoto, Japan, 10-13 December 1996, p. 433.

150. Didier Dubois and Henri Prade A set-theoretic view of belief functions: Logical operations and approximations by fuzzy sets International Journal of General Systems 12 (1986), 193-226. [Approximation]

151. Didier Dubois and Henri Prade Possibility theory Plenum Press, New York, 1988. [Possibility]

152. Didier Dubois and Henri Prade Consonant approximations of belief functions International Journal of Approximate Reasoning 4 (1990), 419-449. [Approximation,possibility]

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155. Didier Dubois, Michel Grabisch, Henri Prade, and Philippe Smets Using the transferable belief model and a qualitative possibility theory approach on an illustrative example: the assessment of the value of a candidate Intern. J. Intell. Systems (2001). [Applications,TBM,possibility]

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308. Ivan Kramosil Measure-theoretic approach to the inversion problem for belief functions Fuzzy Sets and Systems 102 (1999), 363-369.

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310. Ivan Kramosil Belief functions with nonstandard values Proceedings of Qualitative and Quantitative Practical Reasoning (Dav Gabbay, Rudolf Kruse, Andreas Nonnengart, and H. J. Ohlbach, eds.), Bonn, June 1997, pp. 380-391.

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487. W. R. Simpson and J. W. Sheppard The application of evidential reasoning in a portable maintenance aid Proceedings of the IEEE Systems Readiness Technology Conference (V. Jorrand, P.; Sgurev, ed.), San Antonio, TX, USA, 17-21 September 1990, pp. 211-214.

488. Anna Slobodova Conditional belief functions and valuation-based systems Tech. report, Institute of Control Theory and Robotics, Slovak Academy of Sciences, Bratislava, SK, 1994. [Conditioning]

489. Anna Slobodova Multivalued extension of conditional belief functions Proceedings of the International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU / FAPR '97), Bad Honnef, Germany, 9-12 June 1997. [Conditioning]

490. Anna Slobodova A comment on conditioning in the Dempster-Shafer theory Proceedings of the International ICSC Symposia on Intelligent Industrial Automation and Soft Computing (K. Anderson, P.G.; Warwick, ed.), Reading, UK, 26-28 March 1996, pp. 27-31. [Conditioning]

491. Philippe Smets Medical diagnosis : Fuzzy sets and degree of belief Proceedings of MIC'79 (J. Willems, ed.), Wiley, 1979, pp. 185-189.

492. Philippe Smets The degree of belief in a fuzzy event Information Sciences 25 (1981), 1-19.

493. Philippe Smets Medical diagnosis : Fuzzy sets and degrees of belief Int. J. Fuzzy Sets and systems 5 (1981), 259-266.

494. Philippe Smets The combination of evidence in the transferable belief model IEEE Tr. PAMI 12 (1990), 447-458. [Combination,frameworks,TBM]

495. Philippe Smets Varieties of ignorance Information Sciences 57-58 (1991), 135-144.

496. Philippe Smets Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem International Journal of Approximate reasoning 9 (1993), 1-35. [Combination]

497. Philippe Smets Decision making in the TBM: the necessity of the pignistic transformation International Journal of Approximate Reasoning 38(2) (February 2005), 133-147. [Decision,approximation,TBM]

498. Philippe Smets The application of the matrix calculus to belief functions International Journal of Approximate Reasoning 31(1-2) (October 2002), 1-30.

499. Philippe Smets Theory of evidence and medical diagnostic Medical Informatics Europe 78 (1978), 285-291.

500. Philippe Smets Information content of an evidence International Journal of Man Machine Studies 19 (1983), 33-43.

501. Philippe Smets Data fusion in the transferable belief model Proceedings of the 1984 American Control Conference, 1984, pp. 554-555. [Fusion,TBM]

502. Philippe Smets Bayes' theorem generalized for belief functions Proceedings of ECAI'86, vol. 2, 1986, pp. 169-171.

503. Philippe Smets Belief functions Non-Standard Logics for Automated Reasoning (Ph. Smets, A. Mamdani, D. Dubois, and H. Prade, eds.), Academic Press, London, 1988, pp. 253-286. [Logic]

504. Philippe Smets Belief functions versus probability functions Uncertainty and Intelligent Systems (Saitta L. Bouchon B. and Yager R., eds.), Springer Verlag, Berlin, 1988, pp. 17-24.

505. Philippe Smets Constructing the pignistic probability function in a context of uncertainty Uncertainty in Artificial Intelligence 5 (M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer, eds.), Elsevier Science Publishers, 1990, pp. 29-39.

506. Philippe Smets The transferable belief model and possibility theory Proceedings of NAFIPS-90 (Kodrato® Y., ed.), 1990, pp. 215-218. [Frameworks,TBM,possibility]

507. Philippe Smets About updating Proceedings of the 7th conference on Uncertainty in Artificial Intelligence (B. D'ambrosio, Ph. Smets, and Bonissone P. P. and, eds.), 1991, pp. 378-385.

508. Philippe Smets Patterns of reasoning with belief functions Journal of Applied Non-Classical Logic 1:2 (1991), 166-170. [Logic]

509. Philippe Smets Probability of provability and belief functions Logique et Analyse 133-134 (1991), 177-195.

510. Philippe Smets The transferable belief model and other interpretations of Dempster-Shafer's model Uncertainty in Artificial Intelligence 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, and J.F. Lemmer, eds.), North-Holland, Amsterdam, 1991, pp. 375-383. [Frameworks,TBM]

511. Philippe Smets The nature of the unnormalized beliefs encountered in the transferable belief model Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence (AI92) (D'Ambrosio B. Dubois D., Wellmann M.P. and Smets Ph., eds.), 1992, pp. 292-297. [Frameworks,TBM]

512. Philippe Smets Resolving misunderstandings about belief functions International Journal of Approximate Reasoning 6 (1992), 321-34.

513. Philippe Smets The transferable belief model and random sets International Journal of Intelligent Systems 7 (1992), 37-46. [Frameworks,TBM,random sets]

514. Philippe Smets The transferable belief model for expert judgments and reliability problems Reliability Engineering and System Safety 38 (1992), 59-66. [Applications,TBM]

515. Philippe Smets Belief functions : the disjunctive rule of combination and the generalized Bayesian theorem International Journal of Approximate Reasoning 9 (1993), 1-35.

516. Philippe Smets Jeffrey's rule of conditioning generalized to belief functions Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (UAI93) (Mamdani A. Heckerman D., ed.), 1993, pp. 500-505. [Conditioning]

517. Philippe Smets Quantifying beliefs by belief functions : An axiomatic justification Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI'93), 1993, pp. 598-603.

518. Philippe Smets Belief induced by the knowledge of some probabilities Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence (UAI'94) (Lopez de Mantaras R. Heckerman D., Poole D., ed.), 1994, pp. 523-530.

519. Philippe Smets What is Dempster-Shafer's model ? Advances in the Dempster-Shafer Theory of Evidence (Fedrizzi M. Yager R.R. and Kacprzyk J., eds.), Wiley, 1994, pp. 5-34.

520. Philippe Smets The axiomatic justification of the transferable belief model Tech. report, Universite' Libre de Bruxelles, Technical Report TR/IRIDIA/1995-8.1, 1995. [Frameworks,TBM]

521. Philippe Smets Non standard probabilistic and non probabilistic representations of uncertainty Advances in Fuzzy Sets Theory and Technology 3 (Wang P.P., ed.), Duke University, Durham, NC, 1995, pp. 125-154.

522. Philippe Smets Probability, possibility, belief : which for what ? Foundations and Applications of Possibility Theory (Kerre E.E. De Cooman G., Ruan D., ed.), World Scientific, Singapore, 1995, pp. 20-40. [Possibility]

523. Philippe Smets The normative representation of quantified beliefs by belief functions Artificial Intelligence 92 (1997), 229-242.

524. Philippe Smets The application of the transferable belief model to diagnostic problems Int. J. Intelligent Systems 13 (1998), 127-158. [Applications,TBM]

525. Philippe Smets Numerical representation of uncertainty Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 3: Belief Change (Gabbay D., Smets Ph. (Series Eds). Dubois D., and Prade H. (Vol. Eds.), eds.), Kluwer, Doordrecht, 1998, pp. 265-309.

526. Philippe Smets Probability, possibility, belief: Which and where ? Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 1: Quantified Representation of Uncertainty and Imprecision (Gabbay D. and Smets Ph., eds.), Kluwer, Doordrecht, 1998, pp. 1-24. [Possibility]

527. Philippe Smets The transferable belief model for quantified belief representation Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 1: Quantified Representation of Uncertainty and Imprecision (Gabbay D. and Smets Ph., eds.), Kluwer, Doordrecht, 1998, pp. 267-301. [TBM]

528. Philippe Smets Practical uses of belief functions Uncertainty in Artificial Intelligence 15 (Laskey K. B. and Prade H., eds.), 1999, pp. 612-621.

529. Philippe Smets Decision making in a context where uncertainty is represented by belief functions Belief Functions in Business Decisions (Srivastava R., ed.), Physica-Verlag, 2001, pp. 495-504. [Decision]

530. Philippe Smets The a-junctions: the commutative combination operators applicable to belief functions Proceedings of the International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU / FAPR '97) (Nonnengart A. Gabbay D., Kruse R. and Ohlbach H. J., eds.), Bad Honnef, Germany, 9-12 June 1997, pp. 131-153. [Combination]

531. Philippe Smets Probability of deductibility and belief functions Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU'93) (M. Clark, R. Kruse, and S. Moral, eds.), Granada, Spain, 8-10 Nov. 1993, pp. 332-340.

532. Philippe Smets Upper and lower probability functions versus belief functions Proceedings of the International Symposium on Fuzzy Systems and Knowledge Engineering, Guangzhou, China, 1987, pp. 17-21.

533. Philippe Smets Applying the transferable belief model to diagnostic problems Proceedings of 2nd International Workshop on Intelligent Systems and Soft Computing for Nuclear Science and Industry (D. Ruan, P. D'hondt, P. Govaerts, and E.E. Kerre, eds.), Mol, Belgium, 25-27 September 1996, pp. 285-292. [Applications,TBM]

534. Philippe Smets The canonical decomposition of a weighted belief Proceedings of the International Joint Conference on AI (IJCAI'95), Montreal, Canada, 1995, pp. 1896-1901.

535. Philippe Smets The concept of distinct evidence Proceedings of the 4th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'92), Palma de Mallorca, 6-10 July 92, pp. 789-794.

536. Philippe Smets Data fusion in the transferable belief model Proc. 3rd Intern. Conf. Information Fusion, Paris, France 2000, pp. 21-33. [Fusion,TBM]

537. Philippe Smets Transferable belief model versus Bayesian model Proceedings of ECAI 1988 (Kodrato® Y., ed.), Pitman, London, 1988, pp. 495-500. [Frameworks,TBM]

538. Philippe Smets No Dutch Book can be built against the TBM even though update is not obtained by Bayes rule of conditioning SIS, Workshop on Probabilistic Expert Systems (R. Scozzafava, ed.), Roma, Italy, 1993, pp. 181-204. [Conditioning,combination]

539. Philippe Smets Belief functions and generalized Bayes theorem Proceedings of the Second IFSA Congress, Tokyo, Japan, 1987, pp. 404-407.

540. Philippe Smets and Roger Cooke How to derive belief functions within probabilistic frameworks? Proceedings of the International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU / FAPR '97), Bad Honnef, Germany, 9-12 June 1997.

541. Philippe Smets and Yen-Teh Hsia Default reasoning and the transferable belief model Uncertainty in Artificial Intelligence 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, and J.F. Lemmer, eds.), Wiley, 1991, pp. 495-504. [Frameworks,TBM]

542. Philippe Smets, Yen-Teh Hsia, Alessandro Saffiotti, R. Kennes, H. Xu, and E. Emkehrer The transferable belief model Symbolic and Quantitative Approaches to Uncertainty (Kruse R. and Siegel P., eds.), Springer Verlag, Lecture Notes in Computer Science No. 458, Berlin, 1991, pp. 91-96. [Frameworks,TBM]

543. Philippe Smets and Yen-Teh Hsia Defeasible reasoning with belief functions Tech. report, Universite' Libre de Bruxelles, Technical Report TR/IRIDIA/90-9, 1990.

544. Philippe Smets and Robert Kennes The transferable belief model Artificial Intelligence 66 (1994), 191-234. [Frameworks,TBM]

545. Philippe Smets and R. Kruse The transferable belief model for belief representation Uncertainty Management in information systems: from needs to solutions (Motro A. and Smets Ph., eds.), Kluwer, Boston, 1997, pp. 343-368. [Frameworks,TBM]

546. M. J. Smithson Ignorance and uncertainty: Emerging paradigm Springer, New York (NY), 1989.

547. Paul Snow The vulnerability of the Transferable Belief Model to Dutch books Artificial Intelligence 105 (1998), 345-354. [Frameworks,TBM]

548. Leen-Kit Soh, Costas Tsatsoulis, Todd Bowers, and Andrew Williams Representing sea ice knowledge in a Dempster-Shafer belief system Proceedings of IEEE, 1998, pp. 2234-2236.

549. Z. A. Sosnowski and J. S. Walijewski Generating fuzzy decision rules with the use of Dempster-Shafer theory Proceedings of the 13th European Simulation Multiconference 1999 (H. Szczerbicka, ed.), vol. 2, Warsaw, Poland, 1-4 June 1999, pp. 419-426. [Decision,fuzzy]

550. M. Spies Conditional events, conditioning, and random sets IEEE Transactions on Systems, Man, and Cybernetics 24 (1994), 1755-1763. [Conditioning,random sets]

551. R. Spillman Managing uncertainty with belief functions AI Expert 5:5 (May 1990), 44-49.

552. R. P. Srivastava and Glenn Shafer Integrating statistical and nonstatistical audit evidence using belief functions: a case of variable sampling International Journal of Intelligent Systems 9:6 (June 1994), 519-539.

553. R. Stein The Dempster-Shafer theory of evidential reasoning AI Expert 8:8 (August 1993), 26-31.

554. P. R. Stokke, T. A. Boyce, John D. Lowrance, J. William, and K. Ralston Evidential reasoning and project early warning systems Research and Technology Management (1994).

555. P. R. Stokke, T. A. Boyce, John D. Lowrance, J. William, and K. Ralston Industrial project monitoring with evidential reasoning Nordic Advanced Information Technology Magazine 8 (1994), 18-27.

556. E. Straszecka On an application of Dempster-Shafer theory to medical diagnosis support Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT'98), vol. 3, Aachen, Germany: Verlag Mainz, 1998, pp. 1848-1852.

557. Thomas M. Strat The generation of explanations within evidential reasoning systems Proceedings of the Tenth Joint Conference on Artificial Intelligence (Institute of Electrical and Electronical Engineers, eds.), 1987, pp. 1097-1104.

558. Thomas M. Strat Making decisions with belief functions Proceedings of the 5th Workshop on Uncertainty in AI, 1989, pp. 351-360. [Decision]

559. Thomas M. Strat Decision analysis using belief functions International Journal of Approximate Reasoning 4 (1990), 391-417. [Decision]

560. Thomas M. Strat Making decisions with belief functions Uncertainty in Artificial Intelligence 5 (L. N. Kanal M. Henrion, R. D. Schachter and J. F. Lemmers, eds.), North Holland, Amsterdam, 1990. [Decision]

561. Thomas M. Strat Decision analysis using belief functions Advances in the Dempster-Shafer Theory of Evidence, Wiley, New York, 1994. [Decision]

562. Thomas M. Strat Continuous belief functions for evidential reasoning Proceedings of the National Conference on Artificial Intelligence (Institute of Electrical and Electronical Engineers, eds.), August 1984, pp. 308-313.

563. Thomas M. Strat and John D. Lowrance Explaining evidential analysis International Journal of Approximate Reasoning 3 (1989), 299-353.

564. J. J. Sudano Pignistic probability transforms for mixes of low- and high-probability events Proceedings of the Fourth International Conference on Information Fusion (ISIF'01), Montreal, Canada, 2001, pp. 23-27.

565. J. J. Sudano Equivalence between belief theories and nave Bayesian fusion for systems with independent evidential data Proceedings of the Sixth International Conference on Information Fusion (ISIF'03), 2003.

566. Thomas Sudkamp The consistency of Dempster-Shafer updating International Journal of Approximate Reasoning 7 (1992), 19-44.

567. H. Sun and M. Farooq Conjunctive and disjunctive combination rules of evidence Signal Processing, Sensor Fusion, and Target Recognition XIII. Edited by Kadar, Ivan. Proceedings of the SPIE, Volume 5429, pp. 392-401 (I. Kadar, ed.), August 2004. [Combination]

568. P. Suppes and M. Zanotti On using random relations to generate upper and lower probabilities Synthese 36 (1977), 427-440.

569. Bjornar Tessem Interval probability propagation IJAR 7 (1992), 95-120.

570. Bjornar Tessem Approximations for efficient computation in the theory of evidence Artificial Intelligence 61:2 (1993), 315-329.

571. H. M. Thoma Belief function computations Conditional Logic in Expert Systems, North Holland, 1991, pp. 269-308. [Algorithms]

572. Elena Tsiporkova, Bernard De Baets, and Veselka Boeva Dempster's rule of conditioning translated into modal logic Fuzzy Sets and Systems 102 (1999), 317-383. [Logic,combination,conditioning,Dempster]

573. Elena Tsiporkova, Bernard De Baets, and Veselka Boeva Evidence theory in multivalued models of modal logic Journal of Applications of Nonclassical Logic (1999). [Logic]

574. Elena Tsiporkova, Veselka Boeva, and Bernard De Baets Dempster-Shafer theory framed in modal logic International Journal of Approximate Reasoning 21 (1999), 157-175. [Logic]

575. Vakili Approximation of hints Tech. report, Institute for Automation and Operation Research, University of Fribourg, Switzerland, Tech. Report 209, 1993.

576. P. Vasseur, C. Pegard, E. Mouaddib, and L. Delahoche Perceptual organization approach based on Dempster-Shafer theory Pattern Recognition 32 (1999), 1449-1462.

577. Frank Voorbraak A computationally efficient approximation of Dempster-Shafer theory International Journal on Man-Machine Studies 30 (1989), 525-536.

578. Frank Voorbraak On the justification of Dempster's rule of combination Artificial Intelligence 48 (1991), 171-197. [Combination]

579. Peter P. Wakker Dempster-belief functions are based on the principle of complete ignorance Proceedings of the 1st International Symposium on Imprecise Probabilities and Their Applications, Gent, Belgium, 29 June - 2 July 1999, pp. 535-542.

580. Peter Walley Statistical reasoning with imprecise probabilities Chapman and Hall, New York, 1991.

581. Peter Walley Coherent lower (and upper) probabilities University of Warwick, Coventry (U.K.), Statistics Research Report 22, 1981.

582. Peter Walley The elicitation and aggregation of beliefs University of Warwick, Coventry (U.K.), 1982, Statistics Research Report 23.

583. Peter Walley Belief function representations of statistical evidence The Annals of Statistics 15 (1987), 1439-1465.

584. Peter Walley Measures of uncertainty in expert systems Artificial Intelligence 83 (1996), 1-58.

585. Peter Walley Imprecise probabilities The Encyclopedia of Statistical Sciences (C. B. Read, D. L. Banks, and S. Kotz, eds.), Wiley, New York (NY), 1997.

586. Peter Walley Towards a unified theory of imprecise probability International Journal of Approximate Reasoning 24 (2000), 125-148.

587. Peter Walley and Terry L. Fine Towards a frequentist theory of upper and lower probability The Annals of Statistics 10 (1982), 741-761.

588. Chua-Chin Wang and Hen-Son Don A continuous belief function model for evidential reasoning Proceedings of the Ninth Biennial Conference of the Canadian Society for Computational Studies of Intelligence (R.F. Glasgow, J.; Hadley, ed.), Vancouver, BC, Canada, 11-15 May 1992, pp. 113-120.

589. Chua-Chin Wang and Hon-Son Don Evidential reasoning using neural networks Proceedings of IEEE, 1991, pp. 497-502.

590. Chua-Chin Wang and Hon-Son Don A geometrical approach to evidential reasoning Proceedings of IEEE, 1991, pp. 1847-1852.

591. Chua-Chin Wang and Hon-Son Don The majority theorem of centralized multiple bams networks Information Sciences 110 (1998), 179-193.

592. Chua-Chin Wang and Hon-Son Don A robust continuous model for evidential reasoning Journal of Intelligent and Robotic Systems: Theory and Applications 10:2 (June 1994), 147-171.

593. S. Wang and M. Valtorta On the exponential growth rate of Dempster-Shafer belief functions Proceedings of the SPIE - Applications of Artificial Intelligence X: Knowledge-Based Systems, vol. 1707, Orlando, FL, USA, 22-24 April 1992, pp. 15-24.

594. Zhenyuan Wang and George J. Klir Choquet integrals and natural extensions of lower probabilities International Journal of Approximate Reasoning 16 (1997), 137-147.

595. Chua-Chin Wanga and Hon-Son Don A polar model for evidential reasoning Information Sciences 77:3-4 (March 1994), 195-226.

596. L. A. Wasserman Belief functions and statistical inference Canadian Journal of Statistics 18 (1990), 183-196.

597. L. A. Wasserman Comments on Shafer's "Perspectives on the theory and practice of belief functions" International Journal of Approximate Reasoning 6 (1992), 367-375.

598. L. A. Wasserman Prior envelopes based on belief functions Annals of Statistics 18 (1990), 454-464.

599. J. Watada, Y. Kubo, and K. Kuroda Logical approach: to evidential reasoning under a hierarchical structure Proceedings of the International Conference on Data and Knowledge Systems for Manufacturing and Engineering, Vol. 1, Hong Kong, 2-4 May 1994, pp. 285-290. [Logic]

600. T. Weiler Approximation of belief functions IJUFKS 11 (2003), no. 6, 749-777.

601. Leonard P. Wesley Evidential knowledge-based computer vision Optical Engineering 25 (1986), 363-379.

602. Leonard P. Wesley Autonomous locative reasoning: an evidential approach Proceedings of IEEE, 1993, pp. 700-707.

603. H. Whitney On the abstract properties of linear dependence American Journal of Mathematics 57 (1935), 509-533.

604. M. J. Wierman Measuring conflict in evidence theory Proceedings of the Joint 9th IFSA World Congress, Vancouver, BC, Canada, vol. 3, 2001, pp. 1741-1745.

605. S. T. Wierzchon, A. Pacan, and M. A. Klopotek An object-oriented representation framework for hierarchical evidential reasoning Proceedings of the Fourth International Conference (AIMSA '90) (V. Jorrand, P.; Sgurev, ed.), Albena, Bulgaria, 19-22 September 1990, pp. 239-248.

606. S. T. Wierzchon and M. A. Klopotek Modified component valuations in valuation based systems as a way to optimize query processing Journal of Intelligent Information Systems 9 (1997), 157-180.

607. G. G. Wilkinson and J. Megier Evidential reasoning in a pixel classification hierarchy - a potential method for integrating image classifiers and expert system rules based on geographic context International Journal of Remote Sensing 11(10) (October 1990), 1963-1968.

608. P. M. Williams On a new theory of epistemic probability British Journal for the Philosophy of Science 29 (1978), 375-387.

609. P. M. Williams Discussion of Shafer's paper Journal of the Royal Statistical Society B 44 (1982), 322-352.

610. Nic Wilson Chapter 10 : Belief functions algorithms Algorithms for Uncertainty and Defeasible Reasoning

611. Nic Wilson The combination of belief: when and how fast? International Journal of Approximate Reasoning 6 (1992), 377-388. [Combination]

612. Nic Wilson How much do you believe International Journal of Approximate Reasoning 6 (1992), 345-365.

613. Nic Wilson The representation of prior knowledge in a Dempster-Shafer approach TR/Drums Conference, Blanes, 1991.

614. Nic Wilson Decision making with belief functions and pignistic probabilities Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, Granada, 1993, pp. 364-371. [Decision]

615. Nic Wilson and Serafin Moral Fast Markov chain algorithms for calculating Dempster-Shafer belief Proceedings of the 12th European Conference on Artificial Intelligence (ECAI'96) (W. Wahlster, ed.), Budapest, Hungary, 11-16 Aug. 1996, pp. 672-676.

616. S. K. M. Wong and Pawan Lingas Generation of belief functions from qualitative preference relations Proceedings of the Third International Conference (IPMU'90), 1990, pp. 427-429.

617. S. K. M. Wong and Pawan Lingras Representation of qualitative user preference by quantitative belief functions IEEE Transactions on Knowledge and Data Engineering 6:1 (February 1994), 72-78.

618. S. K. M. Wong, Y. Y. Yao, P. Bollmann, and H. C. Burger Axiomatization of qualitative belief structure IEEE Transactions on Systems, Man, and Cybernetics 21 (1990), 726-734.

619. Yan Xia, S. S. Iyengar, and N. E. Brener An event driven integration reasoning scheme for handling dynamic threats in an unstructured environment Artificial Intelligence 95 (1997), 169-186.

620. Hong Xu An efficient implementation of the belief function propagation Proc. of the 7th Uncertainty in Artificial Intelligence (Smets Ph., D'Ambrosio B. D. and Bonissone P. P., eds.), 1991, pp. 425-432.

621. Hong Xu An efficient tool for reasoning with belief functions Proc. of the 4th International Conference on Information Proceeding and Management of Uncertainty in Knowledge-Based Systems, 1992, pp. 65-68.

622. Hong Xu An efficient tool for reasoning with belief functions uncertainty in intelligent systems Advances in the Dempster-Shafer Theory of Evidence (Valverde L. Bouchon-Meunier B. and Yager R. R., eds.), North-Holland: Elsevier Science, 1993, pp. 215-224.

623. Hong Xu Computing marginals from the marginal representation in Markov trees Proc. of the 5th International Conference on Information Proceeding and Management of Uncertainty in Knowledge-Based Systems, 1994, pp. 275-280.

624. Hong Xu Computing marginals from the marginal representation in Markov trees Artificial Intelligence 74 (1995), 177-189.

625. Hong Xu and R. Kennes Steps towards an efficient implementation of Dempster-Shafer theory Advances in the Dempster-Shafer Theory of Evidence (R.R. Yager, M. Fedrizzi, and J. Kacprzyk, eds.), John Wiley and Sons, Inc., 1994, pp. 153-174.

626. Hong Xu and Philippe Smets Evidential reasoning with conditional belief functions Proceedings of the 10th Uncertainty in Artificial Intelligence (Lopez de Mantaras R. and Poole D., eds.), 1994, pp. 598-605. [Conditioning]

627. Hong Xu and Philippe Smets Generating explanations for evidential reasoning Proceedings of the 11th Uncertainty in Artificial Intelligence (Besnard Ph. and Hanks S., eds.), 1995, pp. 574-581.

628. Hong Xu and Philippe Smets Reasoning in evidential networks with conditional belief functions International Journal of Approximate Reasoning 14 (1996), 155-185. [Conditioning,graphical models]

629. Hong Xu and Philippe Smets Some strategies for explanations in evidential reasoning IEEE Transactions on Systems, Man and Cybernetics 26:5 (1996), 599-607.

630. Hong Xu A decision calculus for belief functions in valuation-based systems Proceedings of the 8th Uncertainty in Artificial Intelligence (Dubois D. Wellman M. P. D'Ambrosio B. and Smets Ph., eds.), 1992, pp. 352-359. [Decision]

631. Hong Xu Valuation-based systems for decision analysis using belief functions Decision Support Systems 20 (1997), 165-184. [Decision]

632. Hong Xu, Yen-Teh Hsia, and Philippe Smets A belief-function based decision support system Proceedings of the 9th Uncertainty in Artificial Intelligence (Heckerman D. and Mamdani A, eds.), 1993, pp. 535-542. [Decision]

633. Hong Xu, Yen-Teh Hsia, and Philippe Smets Transferable belief model for decision making in valuation based systems IEEE Transactions on Systems, Man, and Cybernetics 26:6 (1996), 698-707. [Decision,TBM]

634. Ronald R. Yager On the Dempster-Shafer framework and new combination rules Information Sciences 41 (1987), 93-138. [Combination]

635. Ronald R. Yager Decision making under Dempster-Shafer uncertainties Tech. report, Machine Intelligence Institute, Iona College, Tech. Report MII-915. [Decision]

636. Ronald R. Yager Nonmonotonicity and compatibility relations in belief structures

637. Ronald R. Yager Entropy and specificity in a mathematical theory of evidence International Journal of General Systems 9 (1983), 249-260.

638. Ronald R. Yager Arithmetic and other operations on Dempster-Shafer structures International Journal of Man-Machine Studies 25 (1986), 357-366.

639. Ronald R. Yager On the normalization of fuzzy belief structures International Journal of Approximate Reasoning 14 (1996), 127-153.

640. Ronald R. Yager Class of fuzzy measures generated from a Dempster-Shafer belief structure International Journal of Intelligent Systems 14 (1999), 1239-1247.

641. Ronald R. Yager Modeling uncertainty using partial information Information Sciences 121 (1999), 271-294.

642. Ronald R. Yager and D. P. Filev Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory IEEE Transactions on Systems, Man, and Cybernetics 25:8 (1995), 1221-1230. [Logic,fuzzy,applications]

643. Ronald R. Yager The entailment principle for Dempster-Shafer granules International Journal of Intelligent Systems 1 (1986), 247-262.

644. Ronald R. Yager On the Dempster-Shafer framework and new combination rules Information Sciences 41 (1987), 93-138.

645. A. Ben Yaghlane, Thierry Denoeux, and Khaled Mellouli Coarsening approximations of belief functions Proceedings of ECSQARU'2001 (S. Benferhat and P. Besnard, eds.), 2001, pp. 362-373.

646. B. Ben Yaghlane and Khaled Mellouli Belief function propagation in directed evidential networks Proc. of IPMU, 2006.

647. B. Ben Yaghlane, Philippe Smets, and Khaled Mellouli Independence concepts for belief functions Proceedings of Information Processing and Management of Uncertainty (IPMU'2000), 2000. [Independence]

648. Jian-Bo Yang and Madan G. Singh An evidential reasoning approach for multiple-attribute decision making with uncertainty IEEE Transactions on Systems, Man, and Cybernetics 24(1) (1994), 1-18. [Decision]

649. Y. Y. Yao and P. J. Lingras Interpretations of belief functions in the theory of rough sets Information Sciences 104(1-2) (1998), 81-106. [Rough sets]

650. John Yen GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypotheses Communications ACM 32 (1989), 573-585. [Frameworks]

651. John Yen Generalizing the Dempster-Shafer theory to fuzzy sets IEEE Transactions on Systems, Man, and Cybernetics 20(3) (1990), 559-569. [Fuzzy]

652. John Yen Computing generalized belief functions for continuous fuzzy sets International Journal of Approximate Reasoning 6 (1992), 1-31.

653. Lu Yi Evidential reasoning in a multiple classifier system Proceedings of the Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 93) (P.W.H. Chung, G. Lovegrove, and M. Ali, eds.), Edimburgh, UK, 1-4 June 1993, pp. 476-479. [Machine learning]

654. Virginia R. Young and Shaun S. Wang Updating non-additive measures with fuzzy information Fuzzy Sets and Systems 94 (1998), 355-366. [Combination,fuzzy]

655. Chunhai Yu and Fahard Arasta On conditional belief functions International Journal of Approximate Reasoning 10 (1994), 155-172. [Conditioning]

656. Lofti A. Zadeh A mathematical theory of evidence (book review) AI Magazine 5:3 (1984), 81-83. [Foundations]

657. Lofti A. Zadeh Is probability theory sufficient for dealing with uncertainty in AI: a negative view Uncertainty in Artificial Intelligence (L. N. Kanal and J. F. Lemmer, eds.), vol. 2, North-Holland, Amsterdam, 1986, pp. 103-116. [Foundations]

658. Lofti A. Zadeh A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combination AI Magazine 7:2 (1986), 85-90. [Combination]

659. Marco Zaffalon and Enrico Fagiuoli Tree-based credal networks for classification. [Credal sets, graphical models]

660. D. K. Zarley An evidential reasoning system Tech. report, No.206, University of Kansas, 1988. [Frameworks]

661. D. K. Zarley, Y.T. Hsia, and Glenn Shafer Evidential reasoning using DELIEF Proc. Seventh National Conference on Artificial Intelligence, Vol. 1, 1988, pp. 205-209.

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