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].

Foundations, Frameworks, Combination, Random sets, Geometry, Applications, Decision, Machine learning, TBM, Logic, Approximation, Possibility, Fuzzy, Algorithms, Conditioning, IndependenceDownload BibTeX version

J. Aitchinson

Discussion on professor Dempster's paper

Journal of the Royal Statistical Society B30 (1968), 234-237.

[Foundations]

R. Almond

Belief function models for simple series and parallel systems

Department of Statistics, University of Washington, Tech. Report 207 (1991).

R. G. Almond

Fusion and propagation of graphical belief models: an implementation and an example

PhD dissertation, Department of Statistics, Harvard University, 1990.

R. G. Almond

Graphical belief modeling

Chapman and Hall/CRC, 1995.

[Graphical models]

P. An and W. M. Moon

An evidential reasoning structure for integrating geophysical, geological and remote sensing dataProceedings of IEEE, 1993, pp. 1359-1361.

[Applications]

Z. An

Relative evidential support

PhD dissertation, University of Ulster, 1991.

[Foundations]

Z. An, D. A. Bell, and J. G. Hughes

Relation-based evidential reasoningInternational Journal of Approximate Reasoning8 (1993), 231-251.

[Frameworks]

K. A. Andersen and J. N. Hooker

A linear programming framework for logics of uncertaintyDecision Support Systems16 (1996), 39-53.

[Logic]

A. Ayoun and Philippe Smets

Data association in multi-target detection using the transferable belief modelIntern. J. Intell. Systems(2001).

[Applications,TBM]

J. F. Baldwin

Evidential support logical programmingFuzzy Sets and Systems24 (1985), 1-26.

[Logic]

J. F. Baldwin

Towards a general theory of evidential reasoningProceedings of IPMU'90(B. Bouchon-Meunier, R.R. Yager, and L.A. Zadeh, eds.), Paris, France, 2-6 July 1990, pp. 360-369.

[Foundations]

J. F. Baldwin

Combining evidences for evidential reasoningInternational Journal of Intelligent SystemsVol. 6, No. 6 (September 1991), 569-616.

[Combination]

J. A. Barnett

Computational methods for a mathematical theory of evidenceProc. of the 7th National Conference on Artificial Intelligence (AAAI-88), 1981, pp. 868-875.

[Algorithms]

P. Baroni

Extending consonant approximations to capacitiesProceedings of IPMU'04, 2004, pp. 1127-1134.

[Possibility,approximation]

M. Bauer

A Dempster-Shafer approach to modeling agent preferences for plan recognitionUser Modeling and User-Adapted InteractionVol. 5, No. 3-4 (1995), 317-348.

[Applications]

D. A. Bell, J. W. Guan, and G. M. Shapcott

Using the Dempster-Shafer orthogonal sum for reasoning which involves spaceKybernetes27:5 (1998), 511-526.

[Combination]

D. A. Bell, J. W. Guan, and Suk Kyoon Lee

Generalized union and project operations for pooling uncertain and imprecise informationData and Knowledge Engineering18 (1996), 89-117.

S. Benferhat, Alessandro Saffiotti, and Philippe Smets

Belief functions and default reasoning, Montreal, Canada, 1995, pp. 19-26.

Proc. of the 11th Conf. on Uncertainty in AI

[Logic]

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]

R. J. Beran

On distribution-free statistical inference with upper and lower probabilities42 (1971), 157-168.

Annals of Mathematical Statistics

[Foundations,upper-lower]

Berger

Robust bayesian analysis: Sensitivity to the prior25 (1990), 303-328.

Journal of Statistical Planning and Inference

P. Besnard and Jurg Kohlas

Evidence theory based on general consequence relations6 (1995), no. 2, 119-135.

Int. J. of Foundations of Computer Science

[Foundations]

B. Besserer, S. Estable, and B. Ulmer

Multiple knowledge sources and evidential reasoning for shape recognition, 1993, pp. 624-631

Proceedings of IEEE

[Applications]

Elisabetta Binaghi and Paolo Madella Fuzzy Dempster-Shafer reasoning for rule-based classifiersInternational Journal of Intelligent Systems14 (1999), 559-583.

[Fuzzy,machine learning]

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

[Foundations]

P. Black An examination of belief functions and other monotone capacities

PhD dissertation, Department of Statistics, Carnegie Mellon University, 1996, Pgh. PA 15213.

[Foundations]

P. Black Geometric structure of lower probabilitiesRandom Sets: Theory and Applications(Goutsias, Malher, and Nguyen, eds.), Springer, 1997, pp. 361-383.

[Geometry]

Michael Boshra and Hong Zhang Accommodating uncertainty in pixel-based verification of 3-D object hypothesesPattern Recognition Letters20 (1999), 689-698.

[Applications,vision]

E. Bosse and J. Roy Fusion of identity declarations from dissimilar sources using the Dempster-Shafer theoryOptical Engineering36:3 (March 1997), 648-657.

[Fusion]

M. Bruning and Dieter Denneberg Max-min sigma-additive representation of monotone measuresStatistical Papers34 (2002), 23-35.

[Combinatorics]

Noel Bryson and Ayodele Mobolurin Qualitative discriminant approach for generating quantitative belief functionsIEEE Transactions on Knowledge and Data Engineering10 (1998), 345-348.

[Inference]

A. Bundy Incidence calculus: A mechanism for probability reasoningJournal of automated reasoning1 (1985), 263-283.

[Frameworks]

R. Buxton Modelling uncertainty in expert systemsInternational Journal of Man-Machine Studies31 (1989), 415-476.

C. Camerer and M. Weber Recent developments in modeling preferences: uncertainty and ambiguityJournal of Risk and Uncertainty5 (1992), 325-370.

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

[Foundations]

Lucas Caro and Araabi Babak Nadjar Generalization of the Dempster-Shafer theory: a fuzzy-valued measureIEEE Transactions on Fuzzy Systems7 (1999), 255-270.

[Fuzzy]

W. F. Caselton and W. Luo Decision making with imprecise probabilities: Dempster-Shafer theory and applicationWater Resources Research28 (1992), 3071-3083.

[Decision]

Marco E. G. V. Cattaneo Combining belief functions issued from dependent sourcesProc. of ISIPTA, 2003, pp. 133-147.

[Combination,independence]

A. Chateauneuf and J.-C. Vergnaud Ambiguity reduction through new statistical dataInternational Journal of Approximate Reasoning24 (2000), 283-299.

[Inference]

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

Y. Y. Chen Statistical inference based on the possibility and belief measuresTransactions of the American Mathematical Society347 (1995), 1855-1863.

[Possibility,inference]

B. R. Cobb and Prakash P. Shenoy A comparison of Bayesian and belief function reasoningInformation Systems Frontiers5 (2003), no. 4, 345-358.

[Approximation]

Fabio G. Cozman and Serafin Moral

Reasoning with imprecise probabilitiesInternational Journal of Approximate Reasoning24 (2000), 121-123.

[Foundations,frameworks]

H. H. Crapo and Gian-Carlo Rota

On the foundations of combinatorial theory: combinatorial geometries

M.I.T. Press, Cambridge, Mass., 1970.

[Combinatorics]

Valerie Cross and Thomas Sudkamp

Compatibility and aggregation in fuzzy evidential reasoningProceedings of IEEE, 1991, pp. 1901-1906.

[Fuzzy,combination]

Fabio Cuzzolin

Lattice modularity and linear independence18th British Combinatorial Conference, Brighton, UK, 2001.

[Independence,combinatorics]

Fabio Cuzzolin

Visions of a generalized probability theory

PhD dissertation, Universitą di Padova, Dipartimento di Elettronica e Informatica, 19 February 2001.

Fabio Cuzzolin Geometry of Dempster's rule of combination

IEEE Transactions on Systems, Man and Cybernetics part B34 (2004), no. 2, 961-977.

[Combination,geometry]

Fabio Cuzzolin Algebraic structure of the families of compatible frames of discernment

Annals of Mathematics and Artificial Intelligence45(1-2) (2005), 241-274.

[Combinatorics]

Fabio Cuzzolin

Geometry of relative plausibility and relative belief of singletons

Annals of Mathematics and Artificial Intelligence(2010)

[Geometry,approximation]

Fabio Cuzzolin A geometric approach to the theory of evidence

IEEE Transactions on Systems, Man, and Cybernetics - Part C38 (2008), no. 4, 522-534.

[Geometry,frameworks]

Fabio Cuzzolin Three alternative combinatorial formulations of the theory of evidence

Intelligent Decision Analysis journal(2010).

[Foundations,combinatorics]

Fabio Cuzzolin

Semantics of the relative belief of singletons

International Workshop on Uncertainty and Logic UNCLOG'08, Kanazawa, Japan, 2008.

[Approximation]

Fabio Cuzzolin

Complexes of outer consonant approximations

Proceedings of ECSQARU'09, 2009.

[Geometry,approximation]

Fabio Cuzzolin The geometry of consonant belief functions: simplicial complexes of necessity measures

Fuzzy Sets and Systems(2010).

[Geometry,possibility]

Fabio Cuzzolin Credal semantics of Bayesian transformations in terms of probability intervals

IEEE Transactions on Systems, Man, and Cybernetics - Part B(2010).

[Geometry,approximation]

Fabio Cuzzolin

Geometry of upper probabilitiesProceedings of the 3rd International Symposium on Imprecise Probabilities and Their Applications (ISIPTA'03), July 2003.

[Geometry,upper-lower]

Fabio Cuzzolin

Geometry of Dempster's ruleProceedings of FSDK'02, Singapore, 18-22 November 2002.

[Geometry,combination]

Fabio Cuzzolin

Families of compatible frames of discernment as semimodular latticesProc. of the International Conference of the Royal Statistical Society (RSS2000), September 2000.

[Combinatorics]

Wagner Texeira da Silva and Ruy Luiz Milidiu

Algorithms for combining belief functionsInternational Journal of Approximate Reasoning7 (1992), 73-94.

[Combination,algorithms]

Milan Daniel

On transformations of belief functions to probabilitiesInternational Journal of Intelligent Systems, special issue on Uncertainty Processing.

[Approximation]

Milan Daniel

Transformations of belief functions to probabilities

Tech. report, Institute of Computer Science, Academy of Sciences of the Csech Republic.

[Approximation]

Milan Daniel

Consistency of probabilistic transformations of belief functionsProceedings of IPMU, 2004, pp. 1135-1142.

[Approximation]

L. de Campos, J. Huete, and Serafin Moral

Probability intervals: a tool for uncertain reasoningInt. J. Uncertainty Fuzziness Knowledge-Based Syst.1 (1994), 167-196.

[Intervals,frameworks]

Gert de Cooman and D. Aeyels

A random set description of a possibility measure and its natural extension(1998)

[Possibility,random sets]

Gert de Cooman and Marco Zaffalon

Updating beliefs with incomplete observationsArtif. Intell.159 (2004), no. 1-2, 75-125.

[Combination]

F. Dupin de Saint Cyr, J. Lang, and N. Schiex

Penalty logic and its link with Dempster-Shafer theoryProceedings of UAI'94, 1994, pp. 204-211.

[Logic]

Arthur P. Dempster

New methods for reasoning towards posterior distributions based on sample dataAnnals of Mathematical Statistics37 (1966), 355-374.

Arthur P. Dempster

Upper and lower probability inferences based on a sample from a finite univariate populationBiometrika54 (1967), 515-528.

Arthur P. Dempster

Bayes, Fischer and belief functionsBayesian and Likelihood Methods in Statistics and Economics (S. J. Press S. Geisser, J. S. Hodges and A. Zellner, eds.), 1990.

Arthur P. Dempster

Normal belief functions and the Kalman filter

Tech. report, Department of Statistics, Harvard Univerisity, Cambridge, MA, 1990.

Arthur P. Dempster

Upper and lower probabilities induced by a multivariate mappingAnnals of Mathematical Statistics38 (1967), 325-339.

Arthur P. Dempster

A generalization of Bayesian inferenceJournal of the Royal Statistical Society, Series B30 (1968), 205-247.

Arthur P. Dempster

Upper and lower probabilities generated by a random closed intervalAnnals of Mathematical Statistics39 (1968), 957-966.

Arthur P. Dempster

Upper and lower probabilities inferences for families of hypothesis with monotone density ratiosAnnals of Mathematical Statistics40 (1969), 953-969.

Arthur P. Dempster

Lindley's paradox: CommentJournal of the American Statistical Association77:378 (June 1982), 339-341.

Arthur P. Dempster and Augustine Kong

Uncertain evidence and artificial analysis

Tech. report, S-108, Department of Statistics, Harvard University, 1986.

C. Van den Acker

Belief function representation of statistical audit evidenceInternational Journal of Intelligent Systems15 (2000), 277-290.

Dieter Denneberg

Totally monotone core and products of monotone measuresInternational Journal of Approximate Reasoning24 (2000), 273-281.

Dieter Denneberg and Michel Grabisch

Interaction transform of set functions over a finite setInformation Sciences121 (1999), 149-170.

Thierry Denoeux

Construction of predictive belief functions using a frequentist approachIPMU, 2006.

Thierry Denoeux

Conjunctive and disjunctive combination of belief functions induced by non distinct bodies of evidenceArtificial Intelligence(2007).

[Combination]

Thierry Denoeux

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

Thierry Denoeux

A k-nearest neighbour classification rule based on Dempster-Shafer theoryIEEE Transactions on Systems, Man, and Cybernetics25:5 (1995), 804-813.

Thierry Denoeux

Analysis of evidence-theoretic decision rules for pattern classificationPattern Recognition30:7 (1997), 1095-1107.

[Machine learning,decision]

Thierry Denoeux

Reasoning with imprecise belief structuresInternational Journal of Approximate Reasoning20 (1999), 79-111.

Thierry Denoeux

Allowing imprecision in belief representation using fuzzy-valued belief structuresProceedings of IPMU'98, vol. 1, July Paris, 1998, pp. 48-55.

Thierry Denoeux

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

Jean Dezert and F. Smarandache

A new probabilistic transformation of belief mass assignment

(2007).

P. Diaconis

Review of 'a mathematical theory of evidence'Journal of American Statistical Society73:363 (1978), 677-678.

Didier Dubois and Henri Prade

Possibility theory

Plenum Press, New York, 1988.

[Possibility]

Didier Dubois and Henri Prade

Consonant approximations of belief functionsInternational Journal of Approximate Reasoning4 (1990), 419-449.

[Approximation,possibility]

Didier Dubois and Henri Prade

On the combination of evidence in various mathematical frameworksReliability Data Collection and Analysis(J. °amm and T. Luisi, eds.), 1992, pp. 213-241.

[Combination]

Didier Dubois, Henri Prade, and S. Sandri

On possibility/probability transformations

(1993).

[Possibility,approximation]

Didier Dubois and Henri Prade

On the unicity of Dempster's rule of combinationInternational Journal of Intelligent Systems1 (1986), 133-142.

[Combination]

Didier Dubois and Henri Prade

The mean value of a fuzzy numberFuzzy Sets and Systems 24 (1987), 279-300.

Didier Dubois and Henri Prade

Properties of measures of information in evidence and possibility theoriesFuzzy Sets and Systems24 (1987), 161-182.

[Possibility]

Didier Dubois and Henri Prade

Representation and combination of uncertainty with belief functions and possibility measuresComputational Intelligence4 (1988), 244-264.

[Combination,possibility]

Didier Dubois and Henri Prade

Epistemic entrenchment and possibilistic logicArtificial Intelligence50 (1991), 223-239.

[Logic,possibility]

Didier Dubois and Henri Prade

Focusing versus updating in belief function theory

Tech. report, Internal Report IRIT/91-94/R, IRIT, Universite P. Sabatier, Toulouse, France, 1991.

Didier Dubois and Henri Prade

Evidence, knowledge, and belief functionsInternational Journal of Approximate Reasoning6 (1992), 295-319.

Didier Dubois and Henri Prade

A survey of belief revision and updating rules in various uncertainty modelsInternational Journal of Intelligent Systems9 (1994), 61-100.

Didier Dubois and Henri Prade

Bayesian conditioning in possibility theoryFuzzy Sets and Systems92 (1997), 223-240.

[Possibility,conditioning]

Didier Dubois, Henri Prade, and Philippe Smets

New semantics for quantitative possibility theoryProc. of ISIPTA, 2001, pp. 152-161.

[Possibility]

V. Dugat and S. Sandri

Complexity of hierarchical trees in evidence theoryORSA Journal of Computing6 (1994), 37-49.

A. Dutta

Reasoning with imprecise knowledge in expert systemsInformation Sciences37 (1985), 3-24.

W. F. Eddy and G. P. Pei

Structures of rule-based belief functionsIBM J.Res.Develop.30 (1986), 43-101.

H. J. Einhorn and R. M. Hogarth

Decision making under ambiguityJournal of Business59 (1986), S225-S250.

[Decision]

R. Fagin and Joseph Y. Halpern

A new approach to updating beliefsUncertainty in Artificial Intelligence, 6 (L.N. Kanal P.P. Bonissone, M. Henrion and J.F. Lemmer, eds.), 1991, pp. 347-374.

R. Fagin and Joseph Y. Halpern

Uncertainty, belief and probabilityProc. Intl. Joint Conf. in AI (IJCAI-89), 1988, pp. 1161-1167.

Terry L. Fine

Review of a mathematical theory of evidenceBulletin of the American Mathematical Society83 (1977), 667-672.

Bruno De Finetti

Theory of probability

Wiley, London, 1974.

Dale Fixen and Ronald P. S. Mahler

The modified Dempster-Shafer approach to classificationIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans27:1 (January 1997), 96-104.

Philippe Fortemps

Jobshop scheduling with imprecise durations: A fuzzy approachIEEE Transactions on Fuzzy Systems5 (1997), 557-569.

S. Foucher, J.-M. Boucher, and G. B. Benie

Multiscale and multisource classification using Dempster-Shafer theoryProceedings of IEEE, 1999, pp. 124-128.

Fabio Gambino, Giovanni Ulivi, and Marilena Vendittelli

The transferable belief model in ultrasonic map buildingProceedings of IEEE, 1997, pp. 601-608.

[Applications,TBM]

P. Gardenfors, B. Hansson, and N. E. Sahlin

Evidentiary value: philosophical, judicial and psychological aspects of a theory

(1988).

See Ng Geok and Singh Harcharan

Data equalisation with evidence combination for pattern recognitionPattern Recognition Letters19 (1998), 227-235.

[Machine learning]

Peter R. Gillett

Monetary unit sampling: a belief-function implementation for audit and accounting applicationsInternational Journal of Approximate Reasoning25 (2000), 43-70.

M. L. Ginsberg

Non-monotonic reasoning using Dempster's ruleProc. 3rd National Conference on AI (AAAI-84), 1984, pp. 126-129.

Forouzan Golshani, Enrique Cortes-Rello, and Thomas H. Howell

Dynamic route planning with uncertain informationKnowledge-based Systems9 (1996), 223-232.

I. R. Goodman and Hung T. Nguyen

Uncertainty models for knowledge-based systems

North Holland, New York, 1985.

J. Gordon and Edward H. Shortliffe

A method for managing evidential reasoning in a hierarchical hypothesis space: a retrospectiveArtificial Intelligence59:1-2 (February 1993), 43-47.

J. Gordon and Edward H. Shortliffe

A method for managing evidential reasoning in hierarchical hypothesis spacesArtificial Intelligence26 (1985), 323-358.

John Goutsias, Ronald P.S. Mahler, and Hung T. Nguyen

Random sets: theory and applicationsIMA Volumes in Mathematics and Its ApplicationsVol. 97, Springer-Verlag, December 1997.

Michel Grabisch

The Moebius transform on symmetric ordered structures and its application to capacities on finite setsDiscrete Mathematics287 (1-3) (2004), 17-34.

Michel Grabisch

Belief functions on latticesInt. J. of Intelligent Systems (2006).

Michel Grabisch, Hung T. Nguyen, and Elbert A. Walker

Fundamentals of uncertainty calculi with applications to fuzzy inference

Kluwer Academic Publishers, 1995.

J. W. Guan and D. A. Bell

The Dempster-Shafer theory on Boolean algebrasChinese Journal of Advanced Software Research3:4 (November 1996), 313-343.

M. Guironnet, D. Pellerin, and Michčle Rombaut

Camera motion classification based on the transferable belief modelProceedings of EUSIPCO'06, Florence, Italy, 2006.

[Applications,TBM]

V. Ha and P. Haddawy

Theoretical foundations for abstraction-based probabilistic planningProc. of the 12th Conference on Uncertainty in Artificial Intelligence, August 1996, pp. 291-298.

M. Ha-Duong

Hierarchical fusion of expert opinion in the transferable belief model, application on climate sensivity

Working Papers halshs-00112129-v3, HAL, 2006.

[Applications,TBM]

R. Haenni

Towards a unifying theory of logical and probabilistic reasoningProceedings of ISIPTA'05, 2005.

[Logic,foundations]

P. Hajek

Deriving Dempster's ruleProceeding of IPMU'92, 1992, pp. 73-75.

P. Hajek

Getting belief functions from kripke modelsInternational Journal of General Systems24 (1996), 325-327.

P. Hajek

A note on belief functions in mycin-like systemsProceedings of Aplikace Umele Inteligence AI '90, Prague, Czechoslovakia, 20-22 March 1990, pp. 19-26.

P. Hajek and David Harmanec

On belief functions (the present state of Dempster-Shafer theory)Advanced topics in AI(Marik, ed.), Springer-Verlag, 1992.

J. Y. Halpern and R. Fagin

Two views of belief: belief as generalized probability and belief as evidenceArtificial Intelligence54 (1992), 275-317.

J. Y. Halpern

Reasoning about uncertainty

MIT Press, 2003.

David Harmanec, George Klir, and G. Resconi

On modal logic interpretation of Dempster-Shafer theoryInternational Journal of Intelligent Systems9 (1994), 941-951.

[Logic]

David Harmanec, George Klir, and Z. Wang

Modal logic inpterpretation of Dempster-Shafer theory: an infinite caseInternational Journal of Approximate Reasoning14 (1996), 81-93.

[Logic]

David Harmanec and Petr Hajek

A qualitative belief logicInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems(1994).

[Logic]

Stanislaw Heilpern

Representation and application of fuzzy numbersFuzzy Sets and Systems91 (1997), 259-268.

Y. Hel-Or and M. Werman

Constraint fusion for recognition and localization of articulated objectsInt. J. Computer Vision19 (1996), 5-28.

Ebbe Hendon, Hans Jorgen Jacobsen, Birgitte Sloth, and Torben Tranaes

The product of capacities and belief functionsMathematical Social Sciences32 (1996), 95-108.

T. Herron, Terry Seidenfeld, and L. Wasserman

Divisive conditioning: further results on dilationPhilosophy of Science64 (1997), 411-444.

[Conditioning]

H. T. Hestir, Hung T. Nguyen, and G. S. Rogers

A random set formalism for evidential reasoningConditional Logic in Expert Systems, North Holland, 1991, pp. 309-344.

[Random sets]

A. Honda and Michel Grabisch

Entropy of capacities on lattices and set systemsInformation Science(2006).

Lang Hong

Recursive algorithms for information fusion using belief functions with applications to target identificationProceedings of IEEE, 1992, pp. 1052-1057.

Yen-Teh Hsia and Prakash P. Shenoy

An evidential language for expert systemsMethodologies for Intelligent Systems(Ras Z., ed.), North Holland, 1989, pp. 9-16.

Yen-Teh Hsia and Prakash P. Shenoy

Macevidence: A visual evidential language for knowledge-based systems

Tech. report, No 211, School of Business, University of Kansas, 1989.

Yen-Teh Hsia

A belief function semantics for cautious non-monotonicity

Technical Report TR/IRIDIA/91-3, Université Libre de Bruxelles, 1991.

Yen-Teh Hsia

Characterizing belief functions with minimal commitmentProceedings of IJCAI-91, 1991, pp. 1184-1189.

Yen-Teh Hsia and Philippe Smets

Belief functions and non-monotonic reasoning

Université Libre de Bruxelles, Technical Report IRIDIA/TR/1990/3, 1990.

R. Hummel and M. Landy

A statistical viewpoint on the theory of evidenceIEEE Transactions on PAMI(1988), 235-247.

A. Hunter and Weiru Liu

Fusion rules for merging uncertain informationInformation Fusion7(1) (2006), 97-134.

D. Hunter

Dempster-Shafer versus probabilistic logicProceedings of the Third AAAI Uncertainty in Artificial Intelligence Workshop, 1987, pp. 22-29.

[Logic]

V.-N. Huynh, Y. Nakamori, H. Ono, J. Lawry, V. Kreinovich, and H.T. Nguyen (eds.),

Interval / probabilistic uncertainty and non-classical logics

Springer, 2008.

[Logic]

I. Iancu

Prosum-prolog system for uncertainty managementInternational Journal of Intelligent Systems12 (1997), 615-627.

Laurie Webster II, Jen-Gwo Chen, Simon S. Tan, Carolyn Watson, and Andr¶e de Korvin

Validation of authentic reasoning expert systemsInformation Sciences117 (1999), 19-46.

Horace H. S. Ip and Richard C. K. Chiu

Evidential reasoning for facial gesture recognition from cartoon imagesProceedings of IEEE, 1994, pp. 397-401.

Horace H. S. Ip and Hon-Ming Wong

Evidential reasoning in foreign exchange rates forecastingProceedings of IEEE, 1991, pp. 152-159.

J. Y. Jaffray

Application of linear utility theory for belief functionsUncertainty and Intelligent Systems, Springer-Verlag, Berlin, 1988, pp. 1-8.

J. Y. Jaffray

Coherent bets under partially resolving uncertainty and belief functionsTheory and Decision26 (1989), 99-105.

[Decision]

J. Y. Jaffray

Linear utility theory for belief functionsOperation Research Letters8 (1989), 107-112.

J. Y. Jaffray

Bayesian updating and belief functionsIEEE Transactions on Systems, Man and Cybernetics22 (1992), 1144-1152.

J. Y. Jaffray and P. P. Wakker

Decision making with belief functions: compatibility and incompatibility with the sure-thing principleJournal of Risk and Uncertainty8 (1994), 255-271.

[Decision]

Audun Josang, Milan Daniel, and P. Vannoorenberghe

Strategies for combining conflicting dogmatic beliefsProceedings of Fusion 2003, vol. 2, 2003, pp. 1133-1140.

Audun Josang, Simon Pope, and David McAnally

Normalising the consensus operator for belief fusionIPMU, 2006.

Cliff Joslyn

Towards an empirical semantics of possibility through maximum uncertaintyProc. IFSA 1991(R. Lowen and M. Roubens, eds.), vol. A, 1991, pp. 86-89.

[Possibility]

Cliff Joslyn

Possibilistic normalization of inconsistent random intervalsAdvances in Systems Science and Applications(1997), 44-51.

[Possibility]

Cliff Joslyn and Luis Rocha

Towards a formal taxonomy of hybrid uncertainty representationsInformation Sciences110 (1998), 255-277.

A. Jsang and S. Pope

Normalising the consensus operator for belief fusion

(2006).

R. Kennes

Computational aspects of the Moebius transformation of graphsIEEE Transactions on Systems, Man, and Cybernetics22 (1992), 201-223.

D. A. Klain and G.-C. Rota

Introduction to geometric probability

Cambridge University Press, 1997.

F. Klawonn and E. Schweke

On the axiomatic justification of Dempster's rule of combinationInternational Journal of Intelligent Systems7 (1990), 469-478.

[Combination]

George J. Klir and T. A. Folger

Fuzzy sets, uncertainty and information, Prentice Hall, Englewood Cliffs (NJ), 1988.

George J. Klir and A. Ramer

Uncertainty in the Dempster-Shafer theory: a critical re-examinationInternational Journal of General Systems18 (1990), 155-166.

George J. Klir and B. Yuan

Fuzzy sets and fuzzy logic: theory and applications

Prentice Hall PTR, Upper Saddle River, NJ, 1995.

[Logic,fuzzy]

George J. Klir

Principles of uncertainty: What are they? why do we need them?Fuzzy Sets and Systems74 (1995), 15-31.

George J. Klir

On fuzzy-set interpretation of possibility theoryFuzzy Sets and Systems108 (1999), 263-273.

[Possibility,fuzzy]

George J. Klir, Wang Zhenyuan, and David Harmanec

Constructing fuzzy measures in expert systemsFuzzy Sets and Systems92 (1997), 251-264.

E. T. Kohler and C. T. Leondes

Algorithmic modifications to the theory of evidential reasoningJournal of Algorithms17:2 (September 1994), 269-279.

Jurg Kohlas

Modeling uncertainty for plausible reasoning with belief

Tech. Report 116, Institute for Automation and Operations Research, University of Fribourg, 1986.

Jurg Kohlas

Conditional belief structuresProbability in Engineering and Information Science2 (1988), no. 4, 415-433.

[Conditioning]

Jurg Kohlas

Modeling uncertainty with belief functions in numerical modelsEurop. J. of Operational Research40 (1989), 377-388.

Jurg Kohlas

Evidential reasoning about parametric models

Tech. Report 194, Institute for Automation and Operations Research, University Fribourg, 1992.

Jurg Kohlas

Support and plausibility functions induced by filter-valued mappingsInt. J. of General Systems21 (1993), no. 4, 343-363.

Jurg Kohlas

The mathematical theory of evidence - a short introductionSystem Modelling and Optimization(J. Dolezal, ed.), Chapman and Hall, 1995, pp. 37-53.

Jurg Kohlas

Allocation of arguments and evidence theoryTheoretical Computer Science171 (1997), 221-246.

Jurg Kohlas and P. Besnard

An algebraic study of argumentation systems and evidence theory

Tech. Report 95-13, Institute of Informatics, University of Fribourg, 1995.

Jurg Kohlas and Paul-André Monney

Modeling and reasoning with hints

Tech. Report 174, Institute for Automation and Operations Research, University of Fribourg, 1990.

Jurg Kohlas and Paul-André Monney

Propagating belief functions through constraint systemsInt. J. Approximate Reasoning5 (1991), 433-461.

Jurg Kohlas and Paul-André Monney

A mathematical theory of hints - an approach to the Dempster-Shafer theory of evidenceLecture Notes in Economics and Mathematical Systems, Springer-Verlag, 1995.

Augustine Kong

Multivariate belief functions and graphical models

PhD dissertation, Harvard University, Department of Statistics, 1986.

Ivan Kramosil

Expert systems with non-numerical belief functionsProblems of Control and Information Theory17 (1988), 285-295.

Ivan Kramosil

Possibilistic belief functions generated by direct products of single possibilistic measuresNeural Network World9:6 (1994), 517-525.

[Possibility]

Ivan Kramosil

Approximations of believeability functions under incomplete identification of sets of compatible statesKybernetika31 (1995), 425-450.

[Approximation]

Ivan Kramosil

Dempster-Shafer theory with indiscernible states and observationsInternational Journal of General Systems25 (1996), 147-152.

Ivan Kramosil

Expert systems with non-numerical belief functionsProblems of control and information theory16 (1996), 39-53.

Ivan Kramosil

Belief functions generated by signed measuresFuzzy Sets and Systems92 (1997), 157-166.

Ivan Kramosil

Probabilistic analysis of Dempster-Shafer theorypart one, Academy of Science of the Czech Republic, Technical Report 716, 1997.

Ivan Kramosil

Probabilistic analysis of Dempster-Shafer theory. part two.Academy of Science of the Czech Republic, Technical Report 749, 1998.

Ivan Kramosil

Measure-theoretic approach to the inversion problem for belief functionsFuzzy Sets and Systems102 (1999), 363-369.

Ivan Kramosil

Nonspecificity degrees of basic probability assignments in Dempster-Shafer theoryComputers and Artificial Intelligence18:6 (April-June 1993), 559-574.

Ivan Kramosil

Dempster combination rule for signed belief functionsInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems6:1 (February 1998), 79-102.

[Combination]

Ivan Kramosil

Toward a boolean-valued Dempster-Shafer theoryLOGICA '92(Svoboda V., ed.), Prague, 1993, pp. 110-131.

[Logic]

Ivan Kramosil

A probabilistic analysis of Dempster combination ruleThe Logica. Yearbook 1997(Childers Timothy, ed.), Prague, 1997, pp. 174-187.

[Combination]

David H. Krantz and John Miyamoto

Priors and likelihood ratios as evidenceJournal of the American Statistical Association78 (June 1983), 418-423.

P. Krause and D. Clark

Representing uncertain knowledge

Kluwer, Dordrecht, 1993.

R. Krause and E. Schwecke

Specialization: a new concept for uncertainty handling with belief functionsInternational Journal of General Systems18 (1990), 49-60.

R. Kruse, D. Nauck, and F. Klawonn

Reasoning with massUncertainty in Artificial Intelligence(P. Smets B. D. D'Ambrosio and P. P. Bonissone, eds.), Morgan Kaufmann, San Mateo, CA, 1991, pp. 182-187.

H. Kyburg

Bayesian and non-Bayesian evidential updatingArtificial Intelligence31:3 (1987), 271-294.

M. Lamata and Serafin Moral

Calculus with linguistic probabilities and beliefAdvances in the Dempster-Shafer Theory of Evidence, Wiley, New York, 1994, pp. 133-152.

Kathryn B. Laskey

Beliefs in belief functions: an examination of Shafer's canonical examplesAAAI Third Workshop on Uncertainty in Artificial Intelligence, Seattle, 1987, pp. 39-46.

Kathryn B. Laskey and Paul E. Lehner

Assumptions, beliefs and probabilitiesArtificial Intelligence41 (1989), 65-77.

Chia-Hoang Lee

A comparison of two evidential reasoning schemesArtificial Intelligence35 (1988), 127-134.

E. S. Lee and Q. Zhu

Fuzzy and evidential reasoning

Physica-Verlag, Heidelberg, 1995.

E. Lefevre, O. Colot, and P. Vannoorenberghe

Belief functions combination and conflict managementInformation Fusion Journal3 (2002), no. 2, 149-162.

[Combination,conflict]

E. Lehrer

Updating non-additive probabilities a geometric approachGames and Economic Behavior50 (2005), 42-57.

S. A. Lesh

An evidential theory approach to judgement-based decision making

PhD dissertation, Department of Forestry and Environmental Studies, Duke University, December 1986.

[Decision]

Henry Leung and Jiangfeng Wu

Bayesian and Dempster-Shafer target identification for radar surveillanceIEEE Transactions on Aerospace and Electronic Systems36:2 (April 2000), 432-447.

Isaac Levi

The enterprise of knowledge: An essay on knowledge, credal probability, and chance

The MIT Press, Cambridge, Mass., 1980.

Isaac Levi

Consonance, dissonance and evidentiary mechanismFestschrift for Soren Hallden, Theoria, 1983, pp. 27-42.

Z. Li and L. Uhr

Evidential reasoning in a computer vision systemUncertainty in Artificial Intelligence2 (Lemmer and Kanal, eds.), North Holland, Amsterdam, 1988, pp. 403-412.

Ee-Peng Lim, Jaideep Srivastava, and Shashi Shekar

Resolving attribute incompatibility in database integration: an evidential reasoning approachProceedings of IEEE, 1994, pp. 154-163.

J. S. Liu and Y. Wu

Parameter expansion for data augmentationJournal of the American Statistical Association, vol. 94, 1999, pp. 1264-1274.

Liping Liu

Propagation of Gaussian belief functionsLearning Models from Data: AI and Statistics(D. Fisher and H. J. Lenz, eds.), Springer, New York, 1996, pp. 79-88.

Liping Liu

A theory of Gaussian belief functionsInternational Journal of Approximate Reasoning14 (1996), 95-126.

Liping Liu

Local computation of Gaussian belief functionsInternational Journal of Approximate Reasoning22 (1999), 217-248.

Weiru Liu

Analyzing the degree of conflict among belief functionsArtif. Intell.170 (2006), no. 11, 909-924.

Weiru Liu, D. McBryan, and A. Bundy

Method of assigning incidences, Applied Intelligence 9 (1998), 139-161.

Weiru Liu, Jun Hong, and Micheal F. McTear

An extended framework for evidential reasoning systemsProceedings of IEEE, 1990, pp. 731-737.

K. C. Lo

Agreement and stochastic independence of belief functionsMathematical Social Sciences51(1) (2006), 1-22.

Pierre Loonis, El-Hadi Zahzah, and Jean-Pierre Bonnefoy

Multi-classifiers neural network fusion versus Dempster-Shafer's orthogonal ruleProceedings of IEEE, 1995, pp. 2162-2165.

John D. Lowrance

Automated argument constructionJournal of Statistical Planning Inference20 (1988), 369-387.

John D. Lowrance

Evidential reasoning with gister-cl: A manual

Tech. report, Artificial Intelligence Center, SRI International, 333 Ravenswood Avenue, Menlo Park, CA., 1994.

John D. Lowrance and T. D. Garvey

Evidential reasoning: an implementation for multisensor integration

Tech. report, SRI International, Menlo Park, CA, Technical Note 307, 1983.

John D. Lowrance, T. D. Garvey, and Thomas M. Strat

A framework for evidential reasoning systemsReadings in uncertain reasoning(Shafer and Pearl, eds.), Morgan Kaufman, 1990, pp. 611-618.

Ronald P. S. Mahler

Combining ambiguous evidence with respect to ambiguous a priori knowledge. part ii: Fuzzy logicFuzzy Sets and Systems75 (1995), 319-354.

[Combination]

David A. Maluf

Monotonicity of entropy computations in belief functionsIntelligent Data Analysis1 (1997), 207-213.

G. Matheron

Random sets and integral geometry

Wiley Series in Probability and Mathematical Statistics.

[Geometry,random sets]

Sally McClean and Bryan Scotney

Using evidence theory for the integration of distributed databasesInternational Journal of Intelligent Systems12 (1997), 763-776.

[Applications]

Sally McClean, Bryan Scotney, and Mary Shapcott

Using background knowledge in the aggregation of imprecise evidence in databasesData and Knowledge Engineering32 (2000), 131-143.

T. Melkonyan and R. Chambers

Degree of imprecision: Geometric and algebraic approachesInternational Journal of Approximate Reasoning(2006).

Khaled Mellouli

On the propagation of beliefs in networks using the Dempster-Shafer theory of evidence

PhD dissertation, University of Kansas, School of Business, 1986.

Khaled Mellouli and Zied Elouedi

Pooling experts opinion using Dempster-Shafer theory of evidenceProceedings of IEEE, 1997, pp. 1900-1905.

David Mercier, Thierry Denoeux, and M. Masson

Refined sensor tuning in the belief function framework using contextual discountingProc. of IPMU, 2006.

Pedro Miranda, Michel Grabisch, and P. Gil

On some results of the set of dominating k-additive belief functionsProc. of IPMU, 2004, pp. 625-632.

S. M. Mohiddin and T. S. Dillon

Evidential reasoning using neural networksProceedings of IEEE, 1994, pp. 1600-1606.

Catherine K. Murphy

Combining belief functions when evidence conflictsDecision Support Systems29 (2000), 1-9.

[Combination,conflict]

Robin R. Murphy

Dempster-Shafer theory for sensor fusion in autonomous mobile robotsIEEE Transactions on Robotics and Automation14 (1998), 197-206.

[Fusion,applications]

R. E. Neapolitan

The interpretation and application of belief functionsApplied Artificial Intelligence7:2 (April-June 1993), 195-204.

[Foundations]

Hung T. Nguyen and Philippe Smets

On dynamics of cautious belief and conditional objectsInternational Journal of Approximate Reasoning8 (1993), 89-104.

[Conditioning]

Hung T. Nguyen

On random sets and belief functionsJ. Mathematical Analysis and Applications65 (1978), 531-542.

Hung T. Nguyen and T. Wang

Belief functions and random setsApplications and Theory of Random Sets, The IMA Volumes in Mathematics and its Applications, Vol. 97, Springer, 1997, pp. 243-255.

Pekka Orponen Dempster's rule of combination is NP-completeArtificial Intelligence44 (1990), 245-253.

[Combination]

N. Pal, J. Bezdek, and R. Hemasinha Uncertainty measures for evidential reasoning I: a reviewInternational Journal of Approximate Reasoning7 (1992), 165-183.

[Review]

N. Pal, J. Bezdek, and R. Hemasinha Uncertainty measures for evidential reasoning II: a reviewInternational Journal of Approximate Reasoning8 (1993), 1-16.

[Review]

Simon Parsons and Alessandro Saffiotti A case study in the qualitative verification and debugging of numerical uncertaintyInternational Journal of Approximate Reasoning14 (1996), 187-216.

Judea Pearl On evidential reasoning in a hierarchy of hypothesesArtificial Intelligence28:1 (1986), 9-15.

Judea Pearl Reasoning with belief functions: a critical assessment

UCLA, Technical Report R-136, 1989.

Judea Pearl Reasoning with belief functions: an analysis of compatibilityInternational Journal of Approximate Reasoning4 (1990), 363-389.

Judea Pearl Rejoinder to comments on `reasoning with belief functions: an analysis of compatibility'International Journal of Approximate Reasoning6 (1992), 425-443.

L. Polkowski and A. Skowron Rough mereology: A new paradigm for approximate reasoningInternational Journal of Approximate Reasoning15 (1996), 333-365.

G. Priest, R. Routley, and J. Norman Paraconsistent logic: Essays on the inconsistentPhilosophia Verlag, 1989.

[Logic]

Gregory Provan An analysis of ATMS-based techniques for computing Dempster-Shafer belief functionsProceedings of the International Joint Conference on Artificial Intelligence, 1989.

Gregory Provan An analysis of exact and approximation algorithms for Dempster-Shafer theory

Tech. report, Department of Computer Science, University of British Columbia, Tech. Report 90-15, 1990.

Gregory Provan The validity of Dempster-Shafer belief functionsInternational Journal of Approximate Reasoning6 (1992), 389-399.

Gregory Provan A logic-based analysis of Dempster-Shafer theoryInternational Journal of Approximate Reasoning4 (1990), 451-495.

[Logic]

B. Quost, Thierry Denoeux, and M. Masson One-against-all classifier combination in the framework of belief functionsProc. of IPMU, 2006.

[Machine learning]

Andrej Rakar, Ani Jurii, and Peter Ball¶e Transferable belief model in fault diagnosisEngineering Applications of Artificial Intelligence12 (1999), 555-567.

[Applications,TBM]

Arthur Ramer Uniqueness of information measure in the theory of evidenceRandom Sets and Systems24 (1987), 183-196.

. Arthur Ramer and George J. Klir Measures of discord in the Dempster-Shafer theoryInformation Sciences67 (1993), no. 1-2, 35-50.

Arthur Ramer Text on evidence theory: comparative reviewInternational Journal of Approximate Reasoning14 (1996), 217-220.

Germano Resconi, George J. Klir, U. St. Clair, and David Harmanec On the integration of uncertainty theoriesFuzziness and Knowledge-Based Systems1 (1993), 1-18.

Bruno Ristic and Philippe Smets Belief function theory on the continuous space with an application to model based classificationProc. of IPMU, 2004, pp. 1119-1126.

Bruno Ristic and Philippe Smets The TBM global distance measure for the association of uncertain combat ID declarationsInformation Fusion7(3) (2006), 276-284.

Christoph Roemer and Abraham Kandel Applicability analysis of fuzzy inference by means of generalized Dempster-Shafer theoryIEEE Transactions on Fuzzy Systems3:4 (November 1995), 448-453.

Christopher Roesmer Nonstandard analysis and Dempster-shafer theoryInternational Journal of Intelligent Systems15 (2000), 117-127.

David Ross Random sets without separabilityAnnals of Probability14:3 (July 1986), 1064-1069.

Enrique H. Ruspini, John D. Lowrance, and T. M. Strat Understanding evidential reasoningInternational Journal of Approximate Reasoning6 (1992), 401-424.

Enrique H. Ruspini Epistemic logics, probability and the calculus of evidenceProc. 10th Intl. Joint Conf. on AI (IJCAI-87), 1987, pp. 924-931.

[Logic]

Enrique H. Ruspini The logical foundations of evidential reasoning

SRI International, Menlo Park, CA, Technical Note 408, 1986.

[Logic]

Alessandro Saffiotti A belief-function logic

Universit Libre de Bruxelles, MIT Press, pp. 642-647.

[Logic]

Alessandro Saffiotti A hybrid framework for representing uncertain knowledgeProcs. of the 8th AAAI Conf., Boston, MA, 1990, pp. 653-658.

Alessandro Saffiotti A hybrid belief system for doubtful agentsUncertainty in Knowledge Bases, Lecture Notes in Computer Science251, Springer-Verlag, 1991, pp. 393-402.

Alessandro Saffiotti A belief function logicProceedings of the 10th AAAI Conf., San Jose,CA, 1992, pp. 642-647.

[Logic]

Alessandro Saffiotti, S. Parsons, and E. Umkehrer Comparing uncertainty management techniquesMicrocomputers in Civil Engineering9 (1994), 367-380.

Alessandro Saffiotti and E. Umkehrer PULCINELLA: A general tool for propagation uncertainty in valuation networks

Tech. report, IRIDIA, Libre Universite de Bruxelles, 1991.

Johan Schubert Cluster-based specification techniques in Dempster-Shafer theoryProceedings of ECSQARU'95(C. Froidevaux and J. Kohlas, eds.), 1995.

Johan Schubert On nonspecific evidenceInternational Journal of Intelligent Systems8:6 (1993), 711-725.

Johan Schubert Cluster-based specification techniques in Dempster-Shafer theory for an evidential intelligence analysis of multipletarget tracksAI Communications8:2 (1995), 107-110.

Johan Schubert Finding a posterior domain probability distribution by specifying nonspecific evidenceInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems3:2 (1995), 163-185.

Johan Schubert On ¶rho¶³n a decision-theoretic apparatus of Dempster-Shafer theoryInternational Journal of Approximate Reasoning13 (1995), 185-200.

[Decision]

Johan Schubert Specifying nonspecific evidenceInternational Journal of Intelligent Systems11 (1996), 525-563.

Johan Schubert Managing decomposed belief functionsIPMU, 2006.

Romano Scozzafava Subjective probability versus belief functions in artificial intelligenceInternational Journal of General Systems22:2 (1994), 197-206.

Terry Seidenfeld Some static and dynamic aspects of rubust Bayesian theoryRandom Sets: Theory and Applications(Goutsias, Malher, and Nguyen, eds.),Springer, 1997, pp. 385-406.

Terry Seidenfeld, M. Schervish, and J. Kadane Coherent choice functions under uncertaintyProceedings of ISIPTA'07, 2007.

Terry Seidenfeld and L. Wasserman Dilation for convex sets of probabilitiesAnnals of Statistics21 (1993), 1139-1154.

K. Sentz and S. Ferson Combination of evidence in Dempster-Shafer theory

SANDIA Tech. Report, SAND2002-0835, April 2002.

[Combination]

Glenn Shafer Belief functions and parametric modelsJournal of the Royal Statistical Society, Series B44 (1982), 322-352.

Glenn Shafer A mathematical theory of evidence

Princeton University Press, 1976.

Glenn Shafer Nonadditive probabilities in the work of Bernoulli and LambertArch. History Exact Sci.19 (1978), 309-370.

Glenn Shafer Allocations of probabilityAnnals of Probability7:5 (1979), 827-839.

Glenn Shafer Constructive probabilitySynthese48 (1981), 309-370.

Glenn Shafer Two theories of probabilityPhilosophy of Science Association Proceedings1978 (P. Asquith and I. Hacking, eds.), vol. 2, Philosophy of Science Association, East Lansing (MI), 1981.

Glenn Shafer Belief functions and parametric modelsJournal of the Royal Statistical Society B44 (1982), 322-352.

Glenn Shafer The combination of evidence

School of Business, University of Kansas, Lawrence, KS, Working Paper 162, 1984.

[Combination]

Glenn Shafer Conditional probabilityInternational Statistical Review53 (1985), 261-277.

[Conditioning]

Glenn Shafer Nonadditive probabilityEncyclopedia of Statistical Sciences(Kotz and Johnson, eds.), Wiley, 1985, pp. 6, 271-276.

Glenn Shafer The combination of evidenceInternational Journal of Intelligent Systems1 (1986), 155-179.

[Combination]

Glenn Shafer Belief functions and possibility measuresAnalysis of Fuzzy Information 1: Mathematics and logic(Bezdek, ed.), CRC Press, 1987, pp. 51-84.

[Possibility]

Glenn Shafer Probability judgment in artificial intelligence and expert systemsStatistical Science2 (1987), 3-44.

Glenn Shafer Perspectives on the theory and practice of belief functionsInternational Journal of Approximate Reasoning4 (1990), 323-362.

Glenn Shafer A note on Dempster's Gaussian belief functions

Tech. report, School of Business, University of Kansas, Lawrence, KS, 1992.

Glenn Shafer Rejoinders to comments on `perspectives on the theory and practice of belief functions'International Journal of Approximate Reasoning6 (1992), 445-480.

Glenn Shafer Bayes's two arguments for the rule of conditioningAnnals of Statistics10:4 (December 1982), 1075-1089.

[Conditioning]

Glenn Shafer and R. Logan Implementing Dempster's rule for hierarchical evidenceArtificial Intelligence33 (1987), 271-298.

Glenn Shafer and Prakash P. Shenoy Propagating belief functions using local computationsIEEE Expert1 (1986), (3), 43-52.

Glenn Shafer, Prakash P. Shenoy, and Khaled Mellouli Propagating belief functions in qualitative Markov treesInternational Journal of Approximate Reasoning1 (1987), (4), 349-400.

Glenn Shafer and R. Srivastava The Bayesian and belief-function formalism: A general perspective for auditingAuditing: A Journal of Practice and Theory(1989).

Glenn Shafer and Vladimir Vovk Probability and finance: It's only a game!

Wiley, New York, 2001.

Prakash P. Shenoy and Khaled Mellouli Propagation of belief functions: a distributed approachUncertainty in Artificial Intelligence2 (Lemmer and Kanal, eds.), North Holland, 1988, pp. 325-336.

Prakash P. Shenoy and Glenn Shafer An axiomatic framework for Bayesian and belief function propagationProceedings of the AAAI Workshop of Uncertainty in Artificial Intelligence, 1988, pp. 307-314.

F. K. J. Sheridan A survey of techniques for inference under uncertaintyArtificial Intelligence Review5 (1991), 89-119.

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]

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

Philippe Smets The degree of belief in a fuzzy eventInformation Sciences25 (1981), 1-19.

Philippe Smets Medical diagnosis : Fuzzy sets and degrees of beliefInt. J. Fuzzy Sets and systems5 (1981), 259-266.

Philippe Smets The combination of evidence in the transferable belief modelIEEE Tr. PAMI12 (1990), 447-458.

[Combination,frameworks,TBM]

Philippe Smets Varieties of ignoranceInformation Sciences57-58 (1991), 135-144.

Philippe Smets Belief functions: the disjunctive rule of combination and the generalized Bayesian theoremInternational Journal of Approximate reasoning9 (1993), 1-35.

[Combination]

Philippe Smets Decision making in the TBM: the necessity of the pignistic transformationInternational Journal of Approximate Reasoning38(2) (February 2005), 133-147.

[Decision,approximation,TBM]

Philippe Smets The application of the matrix calculus to belief functionsInternational Journal of Approximate Reasoning31(1-2) (October 2002), 1-30.

Philippe Smets Theory of evidence and medical diagnosticMedical Informatics Europe78 (1978), 285-291.

Philippe Smets Information content of an evidenceInternational Journal of Man Machine Studies19 (1983), 33-43.

Philippe Smets Data fusion in the transferable belief modelProceedings of the 1984 American Control Conference, 1984, pp. 554-555.

[Fusion,TBM]

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

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

[Logic]

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

Philippe Smets The transferable belief model and possibility theoryProceedings of NAFIPS-90(Kodrato® Y., ed.), 1990, pp. 215-218.

[Frameworks,TBM,possibility]

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

Philippe Smets Patterns of reasoning with belief functionsJournal of Applied Non-Classical Logic1:2 (1991), 166-170.

[Logic]

Philippe Smets Probability of provability and belief functionsLogique et Analyse133-134 (1991), 177-195.

Philippe Smets Resolving misunderstandings about belief functionsInternational Journal of Approximate Reasoning6 (1992), 321-34.

Philippe Smets The transferable belief model and random setsInternational Journal of Intelligent Systems7 (1992), 37-46.

[Frameworks,TBM,random sets]

Philippe Smets The transferable belief model for expert judgments and reliability problemsReliability Engineering and System Safety38 (1992), 59-66.

[Applications,TBM]

Philippe Smets Belief functions : the disjunctive rule of combination and the generalized Bayesian theoremInternational Journal of Approximate Reasoning9 (1993), 1-35.

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

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.

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]

Philippe Smets The normative representation of quantified beliefs by belief functionsArtificial Intelligence92 (1997), 229-242.

Philippe Smets The application of the transferable belief model to diagnostic problemsInt. J. Intelligent Systems13 (1998), 127-158.

[Applications,TBM]

Philippe Smets Practical uses of belief functionsUncertainty in Artificial Intelligence15 (Laskey K. B. and Prade H., eds.), 1999, pp. 612-621.

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

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

Philippe Smets Data fusion in the transferable belief modelProc. 3rd Intern. Conf. Information Fusion, Paris, France 2000, pp. 21-33.

[Fusion,TBM]

Philippe Smets Transferable belief model versus Bayesian modelProceedings of ECAI 1988(Kodrato® Y., ed.), Pitman, London, 1988, pp. 495-500.

[Frameworks,TBM]

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

Philippe Smets and Yen-Teh Hsia Defeasible reasoning with belief functions

Tech. report, Universite' Libre de Bruxelles, Technical Report TR/IRIDIA/90-9, 1990.

Philippe Smets and Robert Kennes The transferable belief modelArtificial Intelligence66 (1994), 191-234.

[Frameworks,TBM]

M. J. Smithson Ignorance and uncertainty: Emerging paradigm

Springer, New York (NY), 1989.

Paul Snow The vulnerability of the Transferable Belief Model to Dutch booksArtificial Intelligence105 (1998), 345-354.

[Frameworks,TBM]

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

M. Spies Conditional events, conditioning, and random setsIEEE Transactions on Systems, Man, and Cybernetics24 (1994), 1755-1763.

[Conditioning,random sets]

R. Spillman Managing uncertainty with belief functionsAI Expert5:5 (May 1990), 44-49.

R. Stein The Dempster-Shafer theory of evidential reasoningAI Expert8:8 (August 1993), 26-31.

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

P. R. Stokke, T. A. Boyce, John D. Lowrance, J. William, and K. Ralston Industrial project monitoring with evidential reasoningNordic Advanced Information Technology Magazine8 (1994), 18-27.

Thomas M. Strat Making decisions with belief functionsProceedings of the 5th Workshop on Uncertainty in AI, 1989, pp. 351-360.

[Decision]

Thomas M. Strat Decision analysis using belief functionsInternational Journal of Approximate Reasoning4 (1990), 391-417.

[Decision]

Thomas M. Strat Decision analysis using belief functionsAdvances in the Dempster-Shafer Theory of Evidence, Wiley, New York, 1994.

[Decision]

Thomas M. Strat and John D. Lowrance Explaining evidential analysisInternational Journal of Approximate Reasoning3 (1989), 299-353.

Thomas Sudkamp The consistency of Dempster-Shafer updatingInternational Journal of Approximate Reasoning7 (1992), 19-44.

P. Suppes and M. Zanotti On using random relations to generate upper and lower probabilitiesSynthese36 (1977), 427-440.

Bjornar Tessem Interval probability propagationIJAR7 (1992), 95-120.

Bjornar Tessem Approximations for efficient computation in the theory of evidenceArtificial Intelligence61:2 (1993), 315-329.

H. M. Thoma Belief function computationsConditional Logic in Expert Systems, North Holland, 1991, pp. 269-308.

[Algorithms]

Elena Tsiporkova, Bernard De Baets, and Veselka Boeva Dempster's rule of conditioning translated into modal logicFuzzy Sets and Systems102 (1999), 317-383.

[Logic,combination,conditioning,Dempster]

Elena Tsiporkova, Bernard De Baets, and Veselka Boeva Evidence theory in multivalued models of modal logicJournal of Applications of Nonclassical Logic(1999).

[Logic]

Elena Tsiporkova, Veselka Boeva, and Bernard De Baets Dempster-Shafer theory framed in modal logicInternational Journal of Approximate Reasoning21 (1999), 157-175.

[Logic]

Vakili Approximation of hints

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

P. Vasseur, C. Pegard, E. Mouaddib, and L. Delahoche Perceptual organization approach based on Dempster-Shafer theoryPattern Recognition32 (1999), 1449-1462.

Frank Voorbraak A computationally efficient approximation of Dempster-Shafer theoryInternational Journal on Man-Machine Studies30 (1989), 525-536.

Frank Voorbraak On the justification of Dempster's rule of combinationArtificial Intelligence48 (1991), 171-197.

[Combination]

Peter Walley Statistical reasoning with imprecise probabilities

Chapman and Hall, New York, 1991.

Peter Walley Coherent lower (and upper) probabilities

University of Warwick, Coventry (U.K.), Statistics Research Report 22, 1981.

Peter Walley The elicitation and aggregation of beliefs

University of Warwick, Coventry (U.K.), 1982, Statistics Research Report 23.

Peter Walley Belief function representations of statistical evidenceThe Annals of Statistics15 (1987), 1439-1465.

Peter Walley Measures of uncertainty in expert systemsArtificial Intelligence83 (1996), 1-58.

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

Peter Walley Towards a unified theory of imprecise probabilityInternational Journal of Approximate Reasoning24 (2000), 125-148.

Peter Walley and Terry L. Fine Towards a frequentist theory of upper and lower probabilityThe Annals of Statistics10 (1982), 741-761.

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

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

Chua-Chin Wang and Hon-Son Don The majority theorem of centralized multiple bams networksInformation Sciences110 (1998), 179-193.

Chua-Chin Wang and Hon-Son Don A robust continuous model for evidential reasoningJournal of Intelligent and Robotic Systems: Theory and Applications10:2 (June 1994), 147-171.

Zhenyuan Wang and George J. Klir Choquet integrals and natural extensions of lower probabilitiesInternational Journal of Approximate Reasoning16 (1997), 137-147.

Chua-Chin Wanga and Hon-Son Don A polar model for evidential reasoningInformation Sciences77:3-4 (March 1994), 195-226.

L. A. Wasserman Belief functions and statistical inferenceCanadian Journal of Statistics18 (1990), 183-196.

L. A. Wasserman Comments on Shafer's "Perspectives on the theory and practice of belief functions"International Journal of Approximate Reasoning6 (1992), 367-375.

L. A. Wasserman Prior envelopes based on belief functionsAnnals of Statistics18 (1990), 454-464.

T. Weiler Approximation of belief functionsIJUFKS11 (2003), no. 6, 749-777.

Leonard P. Wesley Evidential knowledge-based computer visionOptical Engineering25 (1986), 363-379.

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

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P. M. Williams Discussion of Shafer's paperJournal of the Royal Statistical Society B44 (1982), 322-352.

Nic Wilson Chapter 10 : Belief functions algorithmsAlgorithms for Uncertainty and Defeasible Reasoning

Nic Wilson The combination of belief: when and how fast?International Journal of Approximate Reasoning6 (1992), 377-388.

[Combination]

Nic Wilson How much do you believeInternational Journal of Approximate Reasoning6 (1992), 345-365.

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

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

S. K. M. Wong and Pawan Lingras Representation of qualitative user preference by quantitative belief functionsIEEE Transactions on Knowledge and Data Engineering6:1 (February 1994), 72-78.

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Yan Xia, S. S. Iyengar, and N. E. Brener An event driven integration reasoning scheme for handling dynamic threats in an unstructured environmentArtificial Intelligence95 (1997), 169-186.

Hong Xu Computing marginals from the marginal representation in Markov treesArtificial Intelligence74 (1995), 177-189.

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

Hong Xu and Philippe Smets Reasoning in evidential networks with conditional belief functionsInternational Journal of Approximate Reasoning14 (1996), 155-185.

[Conditioning,graphical models]

Hong Xu and Philippe Smets Some strategies for explanations in evidential reasoningIEEE Transactions on Systems, Man and Cybernetics26:5 (1996), 599-607.

Hong Xu Valuation-based systems for decision analysis using belief functionsDecision Support Systems20 (1997), 165-184.

[Decision]

Ronald R. Yager On the Dempster-Shafer framework and new combination rulesInformation Sciences41 (1987), 93-138.

[Combination]

Ronald R. Yager Decision making under Dempster-Shafer uncertainties

Tech. report, Machine Intelligence Institute, Iona College, Tech. Report MII-915.

[Decision]

Ronald R. Yager Nonmonotonicity and compatibility relations in belief structures

Ronald R. Yager Entropy and specificity in a mathematical theory of evidenceInternational Journal of General Systems9 (1983), 249-260.

Ronald R. Yager Arithmetic and other operations on Dempster-Shafer structuresInternational Journal of Man-Machine Studies25 (1986), 357-366.

Ronald R. Yager On the normalization of fuzzy belief structuresInternational Journal of Approximate Reasoning14 (1996), 127-153.

Ronald R. Yager Class of fuzzy measures generated from a Dempster-Shafer belief structureInternational Journal of Intelligent Systems14 (1999), 1239-1247.

Ronald R. Yager Modeling uncertainty using partial informationInformation Sciences121 (1999), 271-294.

Ronald R. Yager The entailment principle for Dempster-Shafer granulesInternational Journal of Intelligent Systems1 (1986), 247-262.

Ronald R. Yager On the Dempster-Shafer framework and new combination rulesInformation Sciences41 (1987), 93-138.

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

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

[Independence]

Y. Y. Yao and P. J. Lingras Interpretations of belief functions in the theory of rough setsInformation Sciences104(1-2) (1998), 81-106.

[Rough sets]

John Yen GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypothesesCommunications ACM32 (1989), 573-585.

[Frameworks]

John Yen Generalizing the Dempster-Shafer theory to fuzzy setsIEEE Transactions on Systems, Man, and Cybernetics20(3) (1990), 559-569.

[Fuzzy]

John Yen Computing generalized belief functions for continuous fuzzy setsInternational Journal of Approximate Reasoning6 (1992), 1-31.

Virginia R. Young and Shaun S. Wang Updating non-additive measures with fuzzy informationFuzzy Sets and Systems94 (1998), 355-366.

[Combination,fuzzy]

Chunhai Yu and Fahard Arasta On conditional belief functionsInternational Journal of Approximate Reasoning10 (1994), 155-172.

[Conditioning]

Lofti A. Zadeh A mathematical theory of evidence (book review)AI Magazine5:3 (1984), 81-83.

[Foundations]

Lofti A. Zadeh A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combinationAI Magazine7:2 (1986), 85-90.

[Combination]

Marco Zaffalon and Enrico Fagiuoli Tree-based credal networks for classification.

[Credal sets, graphical models]

D. K. Zarley An evidential reasoning system

Tech. report, No.206, University of Kansas, 1988.

[Frameworks]

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