Belief functions and Theory of evidence - Bibliography






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: 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, Università 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]
    Geometry of Dempster's rule of combination
  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.
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  131. Thierry Denoeux
    Construction of predictive belief functions using a frequentist approach
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  132. Thierry Denoeux
    Conjunctive and disjunctive combination of belief functions induced by non distinct bodies of evidence
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    [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
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    [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
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  137. Thierry Denoeux
    Analysis of evidence-theoretic decision rules for pattern classification
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    [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
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  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
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  147. Jean Dezert and F. Smarandache
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  148. P. Diaconis
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  149. A. F. Dragoni, P. Giorgini, and A. Bolognini
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  150. Didier Dubois and Henri Prade
    A set-theoretic view of belief functions: Logical operations and approximations by fuzzy sets
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  151. Didier Dubois and Henri Prade
    Possibility theory
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    [Possibility]
  152. Didier Dubois and Henri Prade
    Consonant approximations of belief functions
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    [Approximation,possibility]
  153. Didier Dubois and Henri Prade
    On the combination of evidence in various mathematical frameworks
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    [Combination]
  154. Didier Dubois, Henri Prade, and S. Sandri
    On possibility/probability transformations
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    [Possibility,approximation]
  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
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    [Applications,TBM,possibility]
  156. Didier Dubois and Henri Prade
    On several representations of an uncertain body of evidence
    Fuzzy Information and Decision Processes (M. M. Gupta and E. Sanchez, eds.), North Holland, Amsterdam, 1982, pp. 167-181.
  157. Didier Dubois and Henri Prade
    On the unicity of Dempster's rule of combination
    International Journal of Intelligent Systems 1 (1986), 133-142.
    [Combination]
  158. Didier Dubois and Henri Prade
    The mean value of a fuzzy number
    Fuzzy Sets and Systems 24 (1987), 279-300.
  159. Didier Dubois and Henri Prade
    The principle of minimum specificity as a basis for evidential reasoning
    Uncertainty in Knowledge-Based Systems (B. Bouchon and R. R. Yager, eds.), Springer-Verlag, Berlin, 1987, pp. 75-84.
  160. Didier Dubois and Henri Prade
    Properties of measures of information in evidence and possibility theories
    Fuzzy Sets and Systems 24 (1987), 161-182.
    [Possibility]
  161. Didier Dubois and Henri Prade
    Representation and combination of uncertainty with belief functions and possibility measures
    Computational Intelligence 4 (1988), 244-264.
    [Combination,possibility]
  162. Didier Dubois and Henri Prade
    Modeling uncertain and vague knowledge in possibility and evidence theories
    Uncertainty in Artificial Intelligence, volume 4 (R. D. Shachter, T. S. Levitt, L. N. Kanal, and J. F. Lemmer, eds.), North-Holland, 1990, pp. 303-318.
    [Possibility]
  163. Didier Dubois and Henri Prade
    Epistemic entrenchment and possibilistic logic
    Artificial Intelligence 50 (1991), 223-239.
    [Logic,possibility]
  164. Didier Dubois and Henri Prade
    Focusing versus updating in belief function theory
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  165. Didier Dubois and Henri Prade
    Evidence, knowledge, and belief functions
    International Journal of Approximate Reasoning 6 (1992), 295-319.
  166. Didier Dubois and Henri Prade
    A survey of belief revision and updating rules in various uncertainty models
    International Journal of Intelligent Systems 9 (1994), 61-100.
  167. Didier Dubois and Henri Prade
    Bayesian conditioning in possibility theory
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    [Possibility,conditioning]
  168. Didier Dubois, Henri Prade, and Philippe Smets
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    [Possibility]
  169. V. Dugat and S. Sandri
    Complexity of hierarchical trees in evidence theory
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  170. Stephen D. Durham, Jeffery S. Smolka, and Marco Valtorta
    Statistical consistency with Dempster's rule on diagnostic trees having uncertain performance parameters
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    Reasoning with imprecise knowledge in expert systems
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    Decision making under ambiguity
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    [Decision]
  174. Z. Elouedi, K. Mellouli, and Philippe Smets
    Decision trees using belief function theory
    Proceedings of the Eighth International Conference IPMU: Information Processing and Management of Uncertainty in Knowledge-based Systems, Vol. 1, pp. 141-148 (2000).
    [Decision,graphical models]
  175. Z. Elouedi, K. Mellouli, and Philippe Smets
    Classification with belief decision trees
    Proceedings of the Ninth International Conference on Artificial Intelligence: Methodology, Systems, Architectures: AIMSA 2000, Varna, Bulgaria, 2000.
    [Decision,machine learning,graphical models]
  176. R. Fagin and Joseph Y. Halpern
    A new approach to updating beliefs
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  177. R. Fagin and Joseph Y. Halpern
    Uncertainty, belief and probability
    Proc. Intl. Joint Conf. in AI (IJCAI-89), 1988, pp. 1161-1167.
  178. C. Ferrari and Gaetano Chemello
    Coupling fuzzy logic techniques with evidential reasoning for sensor data interpretation
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  179. A. Filippidis
    Fuzzy and Dempster-Shafer evidential reasoning fusion methods for deriving action from surveillance observations
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  180. A. Filippidis
    A comparison of fuzzy and Dempster-Shafer evidential reasoning fusion methods for deriving course of action from surveillance observations
    International Journal of Knowledge-Based Intelligent Engineering Systems 3(4) (October 1999), 215-222.
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    Review of a mathematical theory of evidence
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  183. Dale Fixen and Ronald P. S. Mahler
    The modified Dempster-Shafer approach to classification
    IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 27:1 (January 1997), 96-104.
  184. Philippe Fortemps
    Jobshop scheduling with imprecise durations: A fuzzy approach
    IEEE Transactions on Fuzzy Systems 5 (1997), 557-569.
  185. S. Foucher, J.-M. Boucher, and G. B. Benie
    Multiscale and multisource classification using Dempster-Shafer theory
    Proceedings of IEEE, 1999, pp. 124-128.
  186. Fabio Cuzzolin, Giambattista Gennari, Alessandro Chiuso and Ruggero Frezza
    Integrating shape and dynamic probabilistic models for data association and tracking
    CDC'02, Las Vegas, Nevada, December 2002.
  187. Fabio Gambino, Giovanni Ulivi, and Marilena Vendittelli
    The transferable belief model in ultrasonic map building
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  188. P. Gardenfors, B. Hansson, and N. E. Sahlin
    Evidentiary value: philosophical, judicial and psychological aspects of a theory
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  189. See Ng Geok and Singh Harcharan
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  191. G. Giacinto, R. Paolucci, and F. Roli
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  192. Peter R. Gillett
    Monetary unit sampling: a belief-function implementation for audit and accounting applications
    International Journal of Approximate Reasoning 25 (2000), 43-70.
  193. M. L. Ginsberg
    Non-monotonic reasoning using Dempster's rule
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  194. Forouzan Golshani, Enrique Cortes-Rello, and Thomas H. Howell
    Dynamic route planning with uncertain information
    Knowledge-based Systems 9 (1996), 223-232.
  195. I. R. Goodman and Hung T. Nguyen
    Uncertainty models for knowledge-based systems
    North Holland, New York, 1985.
  196. J. Gordon and Edward H. Shortliffe
    A method for managing evidential reasoning in a hierarchical hypothesis space: a retrospective
    Artificial Intelligence 59:1-2 (February 1993), 43-47.
  197. J. Gordon and Edward H. Shortliffe
    A method for managing evidential reasoning in hierarchical hypothesis spaces
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  198. John Goutsias
    Modeling random shapes: an introduction to random closed set theory
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  199. John Goutsias, Ronald P.S. Mahler, and Hung T. Nguyen
    Random sets: theory and applications
    IMA Volumes in Mathematics and Its Applications Vol. 97, Springer-Verlag, December 1997.
  200. Michel Grabisch
    The Moebius transform on symmetric ordered structures and its application to capacities on finite sets
    Discrete Mathematics 287 (1-3) (2004), 17-34.
  201. Michel Grabisch
    Belief functions on lattices
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    Fundamentals of uncertainty calculi with applications to fuzzy inference
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  203. J. W. Guan, D. A. Bell, and V. R. Lesser
    Evidential reasoning and rule strengths in expert systems
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  204. J. W. Guan and D. A. Bell
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    A linear time algorithm for evidential reasoning in knowledge base systems
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    Evidential reasoning in expert systems: computational methods
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  208. M. Guironnet, D. Pellerin, and Michèle Rombaut
    Camera motion classification based on the transferable belief model
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  209. V. Ha and P. Haddawy
    Geometric foundations for interval-based probabilities
    KR'98: Principles of Knowledge Representation and Reasoning (Anthony G. Cohn, Lenhart Schubert, and Stuart C. Shapiro, eds.), San Francisco, California, 1998, pp. 582-593.
  210. V. Ha and P. Haddawy
    Theoretical foundations for abstraction-based probabilistic planning
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  211. M. Ha-Duong
    Hierarchical fusion of expert opinion in the transferable belief model, application on climate sensivity
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  212. R. Haenni
    Towards a unifying theory of logical and probabilistic reasoning
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    [Logic,foundations]
  213. R. Haenni and N. Lehmann
    Resource bounded and anytime approximation of belief function computations
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    [Algorithms,approximation]
  214. R. Haenni, J.W. Romeijn, G. Wheeler, and J. Williamson
    Possible semantics for a common framework of probabilistic logics
    UncLog'08, International Workshop on Interval/Probabilistic Uncertainty and Non-Classical Logics (Ishikawa, Japan) (V. N. Huynh, Y. Nakamori, H. Ono, J. Lawry, V. Kreinovich, and Hung T. Nguyen, eds.), Advances in Soft Computing, no. 46, pp. 268-279.
    [Logic]
  215. P. Hajek
    Deriving Dempster's rule
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  216. P. Hajek
    Getting belief functions from kripke models
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  217. P. Hajek
    A note on belief functions in mycin-like systems
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  218. P. Hajek and David Harmanec
    On belief functions (the present state of Dempster-Shafer theory)
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  219. J. Y. Halpern and R. Fagin
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  220. J. Y. Halpern
    Reasoning about uncertainty
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    [Logic]
  222. David Harmanec, George Klir, and Z. Wang
    Modal logic inpterpretation of Dempster-Shafer theory: an infinite case
    International Journal of Approximate Reasoning 14 (1996), 81-93.
    [Logic]
  223. David Harmanec
    Toward a characterisation of uncertainty measure for the Dempster-Shafer theory
    Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (S. Besnard, P.; Hanks, ed.), Montreal, Que., Canada, 18-20 August 1995, pp. 255-261.
  224. David Harmanec and Petr Hajek
    A qualitative belief logic
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    [Logic]
  225. Sylvie Le Hegarat-Mascle, Isabelle Bloch, and D. Vidal-Madjar
    Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing
    IEEE Transactions on Geoscience and Remote Sensing 35(4) (July 1997), 1018-1031.
  226. Stanislaw Heilpern
    Representation and application of fuzzy numbers
    Fuzzy Sets and Systems 91 (1997), 259-268.
  227. Y. Hel-Or and M. Werman
    Constraint fusion for recognition and localization of articulated objects
    Int. J. Computer Vision 19 (1996), 5-28.
  228. Ebbe Hendon, Hans Jorgen Jacobsen, Birgitte Sloth, and Torben Tranaes
    The product of capacities and belief functions
    Mathematical Social Sciences 32 (1996), 95-108.
  229. T. Herron, Terry Seidenfeld, and L. Wasserman
    Divisive conditioning: further results on dilation
    Philosophy of Science 64 (1997), 411-444.
    [Conditioning]
  230. H. T. Hestir, Hung T. Nguyen, and G. S. Rogers
    A random set formalism for evidential reasoning
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    [Random sets]
  231. J. Hodges, S. Bridges, C. Sparrow, B. Wooley, B. Tang, and C. Jun
    The development of an expert system for the characterization of containers of contaminated waste
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  232. A. Honda and Michel Grabisch
    Entropy of capacities on lattices and set systems
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  233. Lang Hong
    Recursive algorithms for information fusion using belief functions with applications to target identification
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  234. Takahiko Horiuchi
    Decision rule for pattern classification by integrating interval feature values
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    [Machine learning,decision]
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    An evidential language for expert systems
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  236. Yen-Teh Hsia and Prakash P. Shenoy
    Macevidence: A visual evidential language for knowledge-based systems
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  237. Yen-Teh Hsia
    A belief function semantics for cautious non-monotonicity
    Technical Report TR/IRIDIA/91-3, Université Libre de Bruxelles, 1991.
  238. Yen-Teh Hsia
    Characterizing belief functions with minimal commitment
    Proceedings of IJCAI-91, 1991, pp. 1184-1189.
  239. Yen-Teh Hsia and Philippe Smets
    Belief functions and non-monotonic reasoning
    Université Libre de Bruxelles, Technical Report IRIDIA/TR/1990/3, 1990.
  240. R. Hummel and M. Landy
    A statistical viewpoint on the theory of evidence
    IEEE Transactions on PAMI (1988), 235-247.
  241. A. Hunter and Weiru Liu
    Fusion rules for merging uncertain information
    Information Fusion 7(1) (2006), 97-134.
  242. D. Hunter
    Dempster-Shafer versus probabilistic logic
    Proceedings of the Third AAAI Uncertainty in Artificial Intelligence Workshop, 1987, pp. 22-29.
    [Logic]
  243. V.-N. Huynh, Y. Nakamori, H. Ono, J. Lawry, V. Kreinovich, and H.T. Nguyen (eds.),
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    [Logic]
  244. I. Iancu
    Prosum-prolog system for uncertainty management
    International Journal of Intelligent Systems 12 (1997), 615-627.
  245. Laurie Webster II, Jen-Gwo Chen, Simon S. Tan, Carolyn Watson, and Andr¶e de Korvin
    Validation of authentic reasoning expert systems
    Information Sciences 117 (1999), 19-46.
  246. Horace H. S. Ip and Richard C. K. Chiu
    Evidential reasoning for facial gesture recognition from cartoon images
    Proceedings of IEEE, 1994, pp. 397-401.
  247. Horace H. S. Ip and Hon-Ming Wong
    Evidential reasoning in foreign exchange rates forecasting
    Proceedings of IEEE, 1991, pp. 152-159.
  248. M. Itoh and T. Inagaki
    A new conditioning rule for belief updating in the Dempster-Shafer theory of evidence
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    [Conditioning]
  249. J. Y. Jaffray
    Application of linear utility theory for belief functions
    Uncertainty and Intelligent Systems, Springer-Verlag, Berlin, 1988, pp. 1-8.
  250. J. Y. Jaffray
    Coherent bets under partially resolving uncertainty and belief functions
    Theory and Decision 26 (1989), 99-105.
    [Decision]
  251. J. Y. Jaffray
    Linear utility theory for belief functions
    Operation Research Letters 8 (1989), 107-112.
  252. J. Y. Jaffray
    Bayesian updating and belief functions
    IEEE Transactions on Systems, Man and Cybernetics 22 (1992), 1144-1152.
  253. J. Y. Jaffray and P. P. Wakker
    Decision making with belief functions: compatibility and incompatibility with the sure-thing principle
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    [Decision]
  254. J. Y. Jaffray
    Dynamic decision making with belief functions
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    [Decision]
  255. Audun Josang, Milan Daniel, and P. Vannoorenberghe
    Strategies for combining conflicting dogmatic beliefs
    Proceedings of Fusion 2003, vol. 2, 2003, pp. 1133-1140.
  256. Audun Josang, Simon Pope, and David McAnally
    Normalising the consensus operator for belief fusion
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  257. Cliff Joslyn
    Towards an empirical semantics of possibility through maximum uncertainty
    Proc. IFSA 1991 (R. Lowen and M. Roubens, eds.), vol. A, 1991, pp. 86-89.
    [Possibility]
  258. Cliff Joslyn
    Possibilistic normalization of inconsistent random intervals
    Advances in Systems Science and Applications (1997), 44-51.
    [Possibility]
  259. Cliff Joslyn and George Klir
    Minimal information loss possibilistic approximations of random sets
    Proc. 1992 FUZZ-IEEE Conference, San Diego, 1992, pp. 1081-1088.
    [Possibility,random sets,approximation]
  260. Cliff Joslyn and Luis Rocha
    Towards a formal taxonomy of hybrid uncertainty representations
    Information Sciences 110 (1998), 255-277.
  261. A. Jouan, L. Gagnon, and E. Shahbazian P. Valin
    Fusion of imagery attributes with non-imaging sensor reports by truncated Dempster-Shafer evidential reasoning
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  262. A. Jsang and S. Pope
    Normalising the consensus operator for belief fusion
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  263. R. Kennes
    Evidential reasoning in a categorial perspective: conjunction and disjunction on belief functions
    Uncertainty in Artificial Intelligence 6 (P. Smets, B. D'ambrosio and P. P. Bonissone, eds.), Morgan Kaufann, San Mateo, CA, 1991, pp. 174-181.
  264. R. Kennes
    Computational aspects of the Moebius transformation of graphs
    IEEE Transactions on Systems, Man, and Cybernetics 22 (1992), 201-223.
  265. R. Kennes and Philippe Smets
    Computational aspects of the Moebius transformation
    Uncertainty in Artificial Intelligence 6 (P.P. Bonissone, M. Henrion, L.N. Kanal, and J.F. Lemmer, eds.), Elsevier Science Publishers, 1991, pp. 401-416.
  266. R. Kennes and Philippe Smets
    Fast algorithms for Dempster-Shafer theory
    Uncertainty in Knowledge Bases, Lecture Notes in Computer Science 521 (L.A. Zadeh B. Bouchon-Meunier, R.R. Yager, ed.), Springer-Verlag, Berlin, 1991, pp. 14-23.
  267. D. A. Klain and G.-C. Rota
    Introduction to geometric probability
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  268. F. Klawonn and E. Schweke
    On the axiomatic justification of Dempster's rule of combination
    International Journal of Intelligent Systems 7 (1990), 469-478.
    [Combination]
  269. F. Klawonn and Philippe Smets
    The dynamic of belief in the transferable belief model and specialization-generalization matrices
    Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence (D'Ambrosio B. Dubois D., Wellman M.P. and Smets Ph., eds.), 1992, pp. 130-137.
    [TBM]
  270. George J. Klir
    Dynamic decision making with belief functions
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  287. Jurg Kohlas
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  381. E. Moutogianni and M. Lalmas
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  413. Arthur Ramer
    Uniqueness of information measure in the theory of evidence
    Random Sets and Systems 24 (1987), 183-196.
  414. . Arthur Ramer and George J. Klir
    Measures of discord in the Dempster-Shafer theory
    Information Sciences 67 (1993), no. 1-2, 35-50.
  415. Arthur Ramer
    Text on evidence theory: comparative review
    International Journal of Approximate Reasoning 14 (1996), 217-220.
  416. S. Reece
    Qualitative model-based multisensor data fusion and parameter estimation using infinity -norm Dempster-Shafer evidential reasoning
    Proceedings of the SPIE - Signal Processing, Sensor Fusion, and Target Recognition VI (A. Heckerman, D.; Mamdani, ed.), vol. 3068, Orlando, FL, USA, 21-24 April 1997, pp. 52-63.
  417. Germano Resconi, George J. Klir, U. St. Clair, and David Harmanec
    On the integration of uncertainty theories
    Fuzziness and Knowledge-Based Systems 1 (1993), 1-18.
  418. Germano Resconi, A. J. van der Wal, and D. Ruan
    Speed-up of the monte carlo method by using a physical model of the Dempster-Shafer theory
    International Journal of Intelligent Systems 13 (1998), 221-242.
  419. Germano Resconi, George J. Klir, David Harmanec, and Ute St Clair
    Interpretations of various uncertainty theories using models of modal logic: a summary
    Fuzzy Sets and Systems 80 (1996), 7-14.
    [Logic]
  420. Bruno Ristic and Philippe Smets
    Belief function theory on the continuous space with an application to model based classification
    Proc. of IPMU, 2004, pp. 1119-1126.
  421. Bruno Ristic and Philippe Smets
    The TBM global distance measure for the association of uncertain combat ID declarations
    Information Fusion 7(3) (2006), 276-284.
  422. Christoph Roemer and Abraham Kandel
    Applicability analysis of fuzzy inference by means of generalized Dempster-Shafer theory
    IEEE Transactions on Fuzzy Systems 3:4 (November 1995), 448-453.
  423. Christopher Roesmer
    Nonstandard analysis and Dempster-shafer theory
    International Journal of Intelligent Systems 15 (2000), 117-127.
  424. David Ross
    Random sets without separability
    Annals of Probability 14:3 (July 1986), 1064-1069.
  425. Enrique H. Ruspini, John D. Lowrance, and T. M. Strat
    Understanding evidential reasoning
    International Journal of Approximate Reasoning 6 (1992), 401-424.
  426. Enrique H. Ruspini
    Epistemic logics, probability and the calculus of evidence
    Proc. 10th Intl. Joint Conf. on AI (IJCAI-87), 1987, pp. 924-931.
    [Logic]
  427. Enrique H. Ruspini
    The logical foundations of evidential reasoning
    SRI International, Menlo Park, CA, Technical Note 408, 1986.
    [Logic]
  428. Isabelle Bloch S. Le H¶egarat-Mascle and D. Vidal-Madjar
    Introduction of neighbor- hood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover
    Pattern Recognition 31 (1998), 1811-1823.
    [Applications]
  429. Alessandro Saffiotti
    A belief-function logic
    Universit Libre de Bruxelles, MIT Press, pp. 642-647.
    [Logic]
  430. Alessandro Saffiotti
    A hybrid framework for representing uncertain knowledge
    Procs. of the 8th AAAI Conf., Boston, MA, 1990, pp. 653-658.
  431. Alessandro Saffiotti
    A hybrid belief system for doubtful agents
    Uncertainty in Knowledge Bases, Lecture Notes in Computer Science 251, Springer-Verlag, 1991, pp. 393-402.
  432. Alessandro Saffiotti
    Using Dempster-Shafer theory in knowledge representation
    Uncertainty in Artificial Intelligence 6 (P. Smets B. D'ambrosio and P. P. Bonissone, eds.), Morgan Kaufann, San Mateo, CA, 1991, pp. 417-431.
  433. Alessandro Saffiotti
    A belief function logic
    Proceedings of the 10th AAAI Conf., San Jose,CA, 1992, pp. 642-647.
    [Logic]
  434. Alessandro Saffiotti
    Issues of knowledge representation in Dempster-Shafer's theory
    Advances in the Dempster-Shafer theory of evidence (R.R. Yager, M. Fedrizzi, and J. Kacprzyk, eds.), Wiley, 1994, pp. 415-440.
  435. Alessandro Saffiotti, S. Parsons, and E. Umkehrer
    Comparing uncertainty management techniques
    Microcomputers in Civil Engineering 9 (1994), 367-380.
  436. Alessandro Saffiotti and E. Umkehrer
    PULCINELLA: A general tool for propagation uncertainty in valuation networks
    Tech. report, IRIDIA, Libre Universite de Bruxelles, 1991.
  437. K. Schneider
    Dempster-Shafer analysis for species presence prediction of the winter wren (Troglodytes troglodytes)
    Proceedings of the 1st International Conference on GeoComputation (R.J. Abrahart, ed.), vol. 2, Leeds, UK, 17-19 Sept. 1996, p. 738.
  438. Johan Schubert
    Cluster-based specification techniques in Dempster-Shafer theory
    Proceedings of ECSQARU'95 (C. Froidevaux and J. Kohlas, eds.), 1995.
  439. Johan Schubert
    On nonspecific evidence
    International Journal of Intelligent Systems 8:6 (1993), 711-725.
  440. Johan Schubert
    Cluster-based specification techniques in Dempster-Shafer theory for an evidential intelligence analysis of multipletarget tracks
    PhD dissertation, Royal Institute of Technology, Sweden, 1994.
  441. Johan Schubert
    Cluster-based specification techniques in Dempster-Shafer theory for an evidential intelligence analysis of multipletarget tracks
    AI Communications 8:2 (1995), 107-110.
  442. Johan Schubert
    Finding a posterior domain probability distribution by specifying nonspecific evidence
    International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 3:2 (1995), 163-185.
  443. Johan Schubert
    On ¶rho¶³n a decision-theoretic apparatus of Dempster-Shafer theory
    International Journal of Approximate Reasoning 13 (1995), 185-200.
    [Decision]
  444. Johan Schubert
    Specifying nonspecific evidence
    International Journal of Intelligent Systems 11 (1996), 525-563.
  445. Johan Schubert
    Fast Dempster-Shafer clustering using a neural network structure
    Information, Uncertainty and Fusion (R. R. Yager B. Bouchon-Meunier and L. A. Zadeh, eds.), Kluwer Academic Publishers (SECS 516), Boston, MA, 1999, pp. 419-430.
  446. Johan Schubert
    Managing decomposed belief functions
    IPMU, 2006.
  447. Johan Schubert
    Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure
    Proceedings of the 1999 Information, Decision and Control Conference (IDC'99), Adelaide, Australia, 8-10 February 1999, pp. 401-406.
    [Machine learning]
  448. Johan Schubert
    Creating prototypes for fast classification in Dempster-Shafer clustering
    Proceedings of the International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU / FAPR '97), Bad Honnef, Germany, 9-12 June 1997.
  449. Johan Schubert
    A neural network and iterative optimization hybrid for Dempster- Shafer clustering
    Proceedings of EuroFusion98 International Conference on Data Fusion (EF'98) (J. O'Brien M. Bedworth, ed.), Great Malvern, UK, 6-7 October 1998, pp. 29-36.
  450. Johan Schubert
    Fast Dempster-Shafer clustering using a neural network structure
    Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU'98), Université de La Sorbonne, Paris, France, 6-10 July 1998, pp. 1438-1445.
  451. Romano Scozzafava
    Subjective probability versus belief functions in artificial intelligence
    International Journal of General Systems 22:2 (1994), 197-206.
  452. Terry Seidenfeld
    Some static and dynamic aspects of rubust Bayesian theory
    Random Sets: Theory and Applications (Goutsias, Malher, and Nguyen, eds.),Springer, 1997, pp. 385-406.
  453. Terry Seidenfeld, M. Schervish, and J. Kadane
    Coherent choice functions under uncertainty
    Proceedings of ISIPTA'07, 2007.
  454. Terry Seidenfeld and L. Wasserman
    Dilation for convex sets of probabilities
    Annals of Statistics 21 (1993), 1139-1154.
  455. K. Sentz and S. Ferson
    Combination of evidence in Dempster-Shafer theory
    SANDIA Tech. Report, SAND2002-0835, April 2002.
    [Combination]
  456. Glenn Shafer
    Belief functions and parametric models
    Journal of the Royal Statistical Society, Series B 44 (1982), 322-352.
  457. Glenn Shafer
    A mathematical theory of evidence
    Princeton University Press, 1976.
  458. Glenn Shafer
    A theory of statistical evidence
    Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science (W. L. Harper and C. A. Hooker, eds.), vol. 2, Reidel, Dordrecht, 1976, with discussion, pp. 365-436.
  459. Glenn Shafer
    Nonadditive probabilities in the work of Bernoulli and Lambert
    Arch. History Exact Sci. 19 (1978), 309-370.
  460. Glenn Shafer
    Allocations of probability
    Annals of Probability 7:5 (1979), 827-839.
  461. Glenn Shafer
    Constructive probability
    Synthese 48 (1981), 309-370.
  462. Glenn Shafer
    Two theories of probability
    Philosophy of Science Association Proceedings 1978 (P. Asquith and I. Hacking, eds.), vol. 2, Philosophy of Science Association, East Lansing (MI), 1981.
  463. Glenn Shafer
    Belief functions and parametric models
    Journal of the Royal Statistical Society B 44 (1982), 322-352.
  464. Glenn Shafer
    The combination of evidence
    School of Business, University of Kansas, Lawrence, KS, Working Paper 162, 1984.
    [Combination]
  465. Glenn Shafer
    Conditional probability
    International Statistical Review 53 (1985), 261-277.
    [Conditioning]
  466. Glenn Shafer
    Nonadditive probability
    Encyclopedia of Statistical Sciences (Kotz and Johnson, eds.), Wiley, 1985, pp. 6, 271-276.
  467. Glenn Shafer
    The combination of evidence
    International Journal of Intelligent Systems 1 (1986), 155-179.
    [Combination]
  468. Glenn Shafer
    Belief functions and possibility measures
    Analysis of Fuzzy Information 1: Mathematics and logic (Bezdek, ed.), CRC Press, 1987, pp. 51-84.
    [Possibility]
  469. Glenn Shafer
    Probability judgment in artificial intelligence and expert systems
    Statistical Science 2 (1987), 3-44.
  470. Glenn Shafer
    Perspectives on the theory and practice of belief functions
    International Journal of Approximate Reasoning 4 (1990), 323-362.
  471. Glenn Shafer
    A note on Dempster's Gaussian belief functions
    Tech. report, School of Business, University of Kansas, Lawrence, KS, 1992.
  472. Glenn Shafer
    Rejoinders to comments on `perspectives on the theory and practice of belief functions'
    International Journal of Approximate Reasoning 6 (1992), 445-480.
  473. Glenn Shafer
    Bayes's two arguments for the rule of conditioning
    Annals of Statistics 10:4 (December 1982), 1075-1089.
    [Conditioning]
  474. Glenn Shafer and R. Logan
    Implementing Dempster's rule for hierarchical evidence
    Artificial Intelligence 33 (1987), 271-298.
  475. Glenn Shafer and Prakash P. Shenoy
    Propagating belief functions using local computations
    IEEE Expert 1 (1986), (3), 43-52.
  476. Glenn Shafer, Prakash P. Shenoy, and Khaled Mellouli
    Propagating belief functions in qualitative Markov trees
    International Journal of Approximate Reasoning 1 (1987), (4), 349-400.
  477. Glenn Shafer and R. Srivastava
    The Bayesian and belief-function formalism: A general perspective for auditing
    Auditing: A Journal of Practice and Theory (1989).
  478. Glenn Shafer and Vladimir Vovk
    Probability and finance: It's only a game!
    Wiley, New York, 2001.
  479. Prakash P. Shenoy
    Using Dempster-Shafer's belief function theory in expert systems
    Advances in the Dempster-Shafer Theory of Evidence (M. Fedrizzi R. R. Yager and J. Kacprzyk, eds.), Wiley, New York, 1994, pp. 395-414.
  480. Prakash P. Shenoy and Khaled Mellouli
    Propagation of belief functions: a distributed approach
    Uncertainty in Artificial Intelligence 2 (Lemmer and Kanal, eds.), North Holland, 1988, pp. 325-336.
  481. Prakash P. Shenoy and Glenn Shafer
    An axiomatic framework for Bayesian and belief function propagation
    Proceedings of the AAAI Workshop of Uncertainty in Artificial Intelligence, 1988, pp. 307-314.
  482. Prakash P. Shenoy and Glenn Shafer
    Axioms for probability and belief functions propagation
    Uncertainty in Artificial Intelligence 4 (L. N. Kanal R. D. Shachter, T. S. Lewitt and J. F. Lemmer, eds.), North Holland, Amsterdam, 1990, pp. 159-198.
  483. Prakash P. Shenoy, Glenn Shafer, and Khaled Mellouli
    Propagation of belief functions: a distributed approach
    Proceedings of the AAAI Workshop of Uncertainty in Artificial Intelligence, 1986, pp. 149-160.
  484. F. K. J. Sheridan
    A survey of techniques for inference under uncertainty
    Artificial Intelligence Review 5 (1991), 89-119.
  485. Margaret F. Shipley, Charlene A. Dykman, and Andre' de Korvin
    Project management: using fuzzy logic and the Dempster-Shafer theory of evidence to select team members for the project duration
    Proceedings of IEEE, 1999, pp. 640-644.
    [Fuzzy,applications]
  486. M.-A Simard, J. Couture, and E. Bosse
    Data fusion of multiple sensors attribute information for target identity estimation using a Dempster-Shafer evidential combination algorithm
    Proceedings of the SPIE - Signal and Data Processing of Small Targets (K. Anderson, P.G.; Warwick, ed.), vol. 2759, Orlando, FL, USA, 9-11 April 1996, pp. 577-588.
    [Applications]
  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
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  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
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    [Combination]
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    On using random relations to generate upper and lower probabilities
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  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
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    [Algorithms]
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    Dempster's rule of conditioning translated into modal logic
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    [Logic,combination,conditioning,Dempster]
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    [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
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  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
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  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.
  662. L. M. Zouhal and Thierry Denoeux
    An adaptive k-NN rule based on Dempster-Shafer theory
    Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns (CAIP'95) (R. Hlavac, V.; Sara, ed.), Prague, Czech Republic, 6-8 Sept. 1995, pp. 310-317.
    [Machine learning]
  663. Lalla Meriem Zouhal and Thierry Denoeux
    Evidence-theoretic k-nn rule with parameter optimization
    IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 28 (1998), 263-271.
    [Machine learning]