Evidence theory - an imprecise bibliography




".. it offers a reinterpretation of Dempster's work, a reinterpretation that identifies his lower probabilities as epistemic probabilities or degrees of belief .."


1994
P. Palacharla and P.C. Nelson
Understanding Relations between Fuzzy Logic and Evidential Reasoning Methods
Proceedings of Third IEEE International Conference on Fuzzy Systems, Orlando, FL, pp. 1933-1938, June 1994.

1994
P. Palacharla and P.C. Nelson
Evidential Reasoning in Uncertainty for Data Fusion
Proceedings of the Fifth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Paris, France, pp. 715-720, July 1994.

1991
Hestir, H.T. Nguyen (e-mail) and G.S. Rogers
A random set formalism for evidential reasoning
Conditional Logic in Expert Systems, North Holland (1991), pp. 309-344

1978
Nguyen, H.T. (e-mail)
On Random Sets and Belief Functions
J. Mathematical Analysis and Applications, v. 65, pp.531-542,

1995
Michel Grabisch, Hung T. Nguyen and Elbert A. Walker
Fundamentals of uncertainty calculi with applications to fuzzy inference, book reference
Kluwer Academic Publishers

1991
H.M. Thoma
Belief Function Computations
Conditional Logic in Expert Systems, North Holland (1991), pp. 269-308

1990
J.D. Lowrance, T.D. Garvey and T. M. Strat
A framework for evidential reasoning systemsHTML abstract
Readings in uncertain reasoning, Shafer and Pearl editors, pp. 611-618
Morgan Kaufman

August 1998
D. Pagac, E.M. Nebot and H. Durrant-Whyte
An evidential approach to map-building for autonomous vehicles
IEEE Trans. on Robotics and Automation, Vol.14, No 4, pp. 623-629

1986
P. Fua
Using Probability Density Functions in the Framework of Evidential Reasoning
Uncertainty in Knowledge-Based Systems, Lectures Notes in Computer science, vol. 286, pp. 243--252.

July 1994
P. R. Stokke, T. A. Boyce, J.D. Lowrance, and J. William K. Ralston
Industrial project monitoring with evidential reasoningHTML abstract
Nordic Advanced Information Technology Magazine, vol. 8, pp. 18--27

1994
P. R. Stokke, T. A. Boyce, J.D. Lowrance, and J. William K. Ralston
Evidential reasoning and project early warning systemsHTML abstract
Research and Technology Management

Febuary 1994
J.D. Lowrance
Evidential Reasoning with Gister-CL: A ManualHTML abstract
Artificial Intelligence Center, SRI International, 333 Ravenswood Avenue, Menlo Park, CA.

April 1987
J.D. Lowrance
Evidential Reasoning with Gister: A ManualHTML abstract
Artificial Intelligence Center, SRI International, 333 Ravenswood Avenue, Menlo Park, CA.

1986
J.D. Lowrance, T. D. Garvey, and T. M. Strat
A framework for evidential-reasoning systemsHTML abstract
Proceedings of the National Conference on Artificial Intelligence, (Menlo Park, CA), pp. 896--903
American Association for Artificial Intelligence, August 1986.

1982
J.D. Lowrance and T. D. Garvey
Evidential reasoning: A developing conceptHTML abstract
Proceedings of the Internation Conference on Cybernetics and Society, pp. 6--9
Institute of Electrical and Electronical Engineers, October 1982.

May 1992
E. H. Ruspini, J.D. Lowrance, and T. M. Strat
Understanding evidential reasoningHTML abstract
Artificial Intelligence Center, SRI International, 333 Ravenswood Avenue, Menlo Park, CA.
International Journal of Approximate Reasoning, vol. 6, pp. 401--424.

1987
T. M. Strat
The generation of explanations within evidential reasoning systems
Proceedings of the Tenth Joint Conference on Artificial Intelligence, (Menlo Park, CA), pp. 1097--1104
American Association for Artificial Intelligence, August 1987.

1984
T. M. Strat
Continuous belief functions for evidential reasoning
Proceedings of the National Conference on Artificial Intelligence, (Menlo Park, CA), pp. 308--313
American Association for Artificial Intelligence, August 1984.

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

1991
Saffiotti, A.
A hybrid belief system for doubtful agents
Uncertatiny in Knowledge Bases. Lecture Notes in Computer Science 251. Springer-Verlag, 393-402.

1992
Saffiotti, A.
A Belief Function Logicpostscript
Proceedings of the 10th AAAI Conf. San Jose, CA, 642-647.

1994
Saffiotti, A.
Issues of knowledge representation in Dempster-Shafer's theorypostscript
In: R.R. Yager, M. Fedrizzi and J. Kacprzyk (Eds.) Advances in the Dempster-Shafer theory of evidence. Wiley, 415-440.

1994
Saffiotti, A., Parsons, S. and Umkehrer, E.
Comparing uncertainty management techniques
Microcomputers in Civil Engineering 9, 367-380.

1995
Benferaht, S., Saffiotti, A. and Smets, P.
Belief functions and default reasoningpostscript
Procs. of the 11th Conf. on Uncertainty in AI. Montreal, Canada, 19-26.

1966
Dempster, A.P.
New methods for reasoning toward posterior distributions based on sample data
Annals of Mathematical Statistics, 37, 355-374

1986
Dempster, A.P. and Kong, Augustine
Uncertain evidence and artificial analysis
Department of Accounting and Information Systems, Faculty of Management, Rutgers University, and Department of Statistics, University of Chicago
Research report S-108, Department of Statistics, Harvard University

1989
Hsia, Y. and Prakash P.Shenoy (e-mail)
An evidential language for expert systems
Methodologies for Intelligent Systems, 4, Ras Z. editor, North Holland, 9-16

1989
Hsia, Y. and Shenoy, P.P.
MacEvidence: A visual evidential language for knowledge-based systems
Working paper No 211, School of Business, University of Kansas

1986
Kong, A.
Multivariate belief functions and graphical models
Department of Statistics, University of Chicago
Doctoral dissertation, Department of Statistics, Harvard University

1986
Mellouli, K.
On the propagation of beliefs in networks using the Dempster-Shafer theory of evidence
Doctoral dissertation, School of Business, University of Kansas

1976
Shafer, G. (e-mail)
A mathematical theory of evidence
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Princeton University Press

1987
Shafer, G. and Logan, R.
Implementing Dempster's rule for hierarchical evidence
Artificial Intelligence, 33, 271-298

1987
Shafer, G., Shenoy, P.P. and Mellouli, K.
Propagating belief functions in qualitative Markov trees
International Journal of Approximate Reasoning, 1(4), 349-400

1989
Shenoy, P.P.
On Spohn's rule for revision of beliefs
Working paper No. 213, School of Business, University of Kansas

1986
Shenoy, P.P. and Shafer, G.
Propagating belief functions using local computations
IEEE Expert, 1(3), 43-52

1988
Shenoy, P.P., Shafer, G. and Mellouli, K.
Propagation of belief functions: a distributed approach
Uncertainty in Artificial Intelligence 2, Lemmer and Kanal editors, North Holland, 325-336

1990
Spohn, W.
A general non-probabilistic theory of inductive reasoning
Readings in Uncertain Reasoning

1988
Zarley, D.K.
An evidential reasoning system
Working paper No.206, School of Business, University of Kansas

1988
Zarley, D.K., Hsia, Y.T. and Shafer, G.
Evidential reasoning using DELIEF
Proceeding of the Seventh National Conference on Artificial Intelligence (AAAI-88), 1, 205-209

1989
Biswas, G. and Anand, T.S.
Using the Dempster-Shafer scheme in a mixed-initiative expert system shell
Uncertainty in Artificial Intelligence 3, Kanal et al. editors, North Holland

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

1986
Eddy, W.F. and Pei, G.P.
Structures of rule-based belief functions
IBM J.Res.Develop. 30, 43-101

1988
Fagin, R. and Halpern, J.Y.
Uncertainty, belief and probability
Proc. Intl. Joint Conf. in AI (IJCAI-89), 1161-1167

1984
Ginsberg, M.L.
Non-monotonic reasoning using Dempster's rule
Proc. 3rd National Conference on AI (AAAI-84), 126-129

1988
Laskey, K. and Lehner, P.E.
Belief manteinance: an integrated approach to uncertainty management
Proceeding of the Seventh National Conference on Artificial Intelligence (AAAI-88), 1, 210-214

1983
Levi, I.
Consonance, dissonance and evidentiary mechanism
Festschrift for Soren Hallden, Theoria, 27-42

1986
Pearl, J.
Fusion, propagation and structuring in belief networks
Artificial Intelligence 29, 241-288

1987
Ruspini, E.
Epistemic logics, probability and the calculus of evidence
Artificial Intelligence Center, SRI International, 333 Ravenswood Avenue, Menlo Park, CA.
Proc. 10th Intl. Joint Conf. on AI (IJCAI-87), 924-931

1982
Shafer, G. (e-mail)
Belief functions and parametric models
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Journal of the Royal Statistical Society B.44, 322-352

1987
Shafer, G. (e-mail)
Belief functions and possibility measures
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Analysis of Fuzzy Information, Bezdek editor, 1: Mathematics and logic, 51-84, CRC Press

1989
Shafer, G. and Srivastava, R.
The Bayesian and belief-function formalism: A general perspective for auditing
Auditing: A Journal of Practice and Theory

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

1981
Shafer, G. (e-mail)
Constructive probability
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Synthese, 48, 309-370

1985
Shafer, G. (e-mail)
Nonadditive probability
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Encyclopedia of Statistical Sciences, 6, 271-276, Kotz and Johnson editors, Wiley

1985
Shafer, G. (e-mail)
Conditional probability
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
International Statistical Review, 53, 261-277

1986
Shafer, G. (e-mail)
The combination of evidence
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
International Journal of Intelligent Systems, 1, 155-179

1987
Shafer, G. (e-mail)
Probability judgment in artificial intelligence and expert systems
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
Statistical Science, 2, 3-44

1985
Shafer, G. and Tversky, A.
Languages and designs for probability judgment
Cognitive Science, 9, 309-339

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

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

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

1985
Gordon, J. and Edward H. Shortliffe
A method for managing evidential reasoning in hierarchical hypothesis spaces
Artificial Intelligence, 26, 323-358

1990
Shafer, G. (e-mail)
Perspectives on the theory and practice of belief functions
Department of Accounting and Information Systems, Faculty of Management, Rutgers University
International Journal of Approximate Reasoning

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

G. Choquet.
Theory of capacities
Annales de l'Institut Fourier, 5:131-295, 1953-1954.

D. Denneberg.
Non-Additive Measure and Integral
Kluwer, Dordrecht, 1994.

B. Anger.
Representation of capacities
Mathematische Annalen, 229:245-258, 1977.

A. Chateauneuf.
On the use of capacities in modeling uncertainty aversion and risk aversion
Journal of Mathematical Economics, 20:343-369, 1991.

A. Chateauneuf and J.-Y. Jaffray.
Some characterizations of lower probabilities and other monotone capacities through the use of Möbius inversion
Mathematical Social Sciences, 17:263-283, 1989.

P. J. Huber.
The use of Choquet capacities in statistics
Bulletin of the International Statistical Institute, 45(4):181-188, 1973.

L. A. Wasserman and J. B. Kadane.
Bayes' theorem for Choquet capacities
The Annals of Statistics, 18:1328-1339, 1990.

Ebbe Groes, Hans Jørgen Jacobsen, Birgitte Sloth, and Torben Tranæs
The Product of Capacities and Belief Functions
University of Copenhagen
Mathematical Social Sciences 32, 95-108.

T. Augustin.
Modeling weak information with generalized basic probability assignments
In H. H. Bock and W. Polasek, editors, Data Analysis and Information Systems - Statistical and Conceptual Approaches, pages 101-113. Springer, Heidelberg, 1996.

G. Biswas and T. S. Anand.
Using the Dempster-Shafer scheme in a mixed-initiative expert system shell
In L. N. Kanal, T. S. Levitt, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 3, pages 223-239. North-Holland, Amsterdam, 1989.

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

D. Dubois and H. Prade.
Modeling uncertain and vague knowledge in possibility and evidence theories
In R. D. Shachter, T. S. Levitt, L. N. Kanal, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 4, pages 303-318. North-Holland, Amsterdam, 1990.

D. Dubois and H. Prade.
Evidence, knowledge, and belief functions
International Journal of Approximate Reasoning, 6:295-319, 1992.

T. L. Fine.
Review of A Mathematical Theory of Evidence
Bulletin of the American Mathematical Society, 83:667-672, 1977.

J.-Y. Jaffray.
Bayesian updating and belief functions
IEEE Transactions on Systems, Man and Cybernetics, 22:1144-1152, 1992.

J.-Y. Jaffray and P. P. Wakker.
Decision making with belief functions: compatibility and incompatibility with the sure-thing principle
Journal of Risk and Uncertainty, 8:255-271, 1994.

G. J. Klir and A. Ramer.
Uncertainty in the Dempster-Shafer theory: a critical re-examination
International Journal of General Systems, 18:155-166, 1990.

J. Pearl.
Reasoning with belief functions: an analysis of compatibility
International Journal of Approximate Reasoning, 4:363-389, 1990.

J. Pearl.
Rejoinder to comments on `reasoning with belief functions: an analysis of compatibility'
International Journal of Approximate Reasoning, 6:425-443, 1992.

G. Shafer.
Belief functions and parametric models
Journal of the Royal Statistical Society, Series B, 44:322-352, 1982, with discussion.

G. Shafer.
Perspectives on the theory and practice of belief functions
International Journal of Approximate Reasoning, 4:323-362, 1990.

G. Shafer.
Rejoinders to comments on `perspectives on the theory and practice of belief functions'
International Journal of Approximate Reasoning, 6:445-480, 1992.

G. Shafer and R. Logan.
Implementing Dempster's rule for hierarchical evidence
Artificial Intelligence, 33:271-298, 1987.

P. Shenoy and G. Shafer.
Propagating belief functions with local computations
IEEE Expert, 1(3), 1986.

Ph. Smets.
Belief functions
In Ph. Smets, A. Mamdani, D. Dubois, and H. Prade, editors, Non-Standard Logics for Automated Reasoning, pages 253-286. Academic Press, London, 1988.

Ph. Smets.
The transferable belief model and other interpretations of Dempster-Shafer's model
In P. P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 6, pages 375-383. North-Holland, Amsterdam, 1991.

Ph. Smets.
Resolving misunderstandings about belief functions
International Journal of Approximate Reasoning, 6:321-344, 1992.

T. M. Strat.
Decision analysis using belief functions
International Journal of Approximate Reasoning, 4:391-417, 1990.

F. Voorbraak.
On the justification of Dempster's rule of combination
Artificial Intelligence, 48:171-197, 1991.

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

P. Walley.
Belief function representations of statistical evidence
The Annals of Statistics, 15:1439-1465, 1987.

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

L. A. Wasserman.
Comments on shafer's `perspectives on the theory and practice of belief functions`
International Journal of Approximate Reasoning, 6:367-375, 1992.

N. Wilson.
The combination of belief: when and how fast?
International Journal of Approximate Reasoning, 6:377-388, 1992.

R. J. Beran.
On distribution-free statistical inference with upper and lower probabilities
Annals of Mathematical Statistics, 42:157-168, 1971.

A. P. Dempster.
Upper and lower probabilities induced by a multivalued mapping
Annals of Mathematical Statistics, 38:325-339, 1967.

A. P. Dempster.
Upper and lower probability inferences based on a sample from a finite univariate population
Biometrika, 54:515-528, 1967.

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

P. Walley and T. L. Fine.
Towards a frequentist theory of upper and lower probability
The Annals of Statistics, 10:741-761, 1982.

Jurg Kohlas and Paul-Andrè Monney
A Mathematical Theory of Hints - An Approach to the Dempster-Shafer Theory of Evidence
Lecture Notes in Economics and Mathematical Systems
Springer-Verlag, 1995

Y. Y. Chen.
Statistical inference based on the possibility and belief measures
Transactions of the American Mathematical Society, 347:1855-1863, 1995.

J. Y. Halpern and R. Fagin.
Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence, 54:275-317, 1992.

H. E. Kyburg.
Bayesian and non-Bayesian evidential updating
Artificial Intelligence, 31:271-293, 1987.

I. Levi.
Consonance, dissonance and evidentiary mechanisms
In P. Gärdenfors, B. Hansson, and N.-E. Sahlin, editors, Evidentiary Value, pages 27-43. Gleerups, Lund, Sweden, 1983.

P. Walley.
Statistical Reasoning with Imprecise Probabilities
Chapman and Hall, London, 1991.

P. Walley.
Measures of uncertainty in expert systems
Artificial Intelligence, 83:1-58, 1996.

J. Kohlas.
Allocation of Arguments and Evidence Theory,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Theoretical Computer Science, 171, 221--246. 1997.

P. Besnard and J. Kohlas.
Evidence Theory Based on General Consequence Relations,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Int. J. of Foundations of Computer Science, 6(2), 119-135. 1995.

J. Kohlas and P.A. Monney.
Theory of Evidence - a Survey of its Mathematical Foundations, Applications and Computational Anaylsis,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
ZOR- Mathematical Methods of Operations Research, 39, 35--68. 1994.

J. Kohlas.
Support-and Plausibility Functions Induced by Filter-Valued Mappings,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Int. J. of General Systems, 21(4), 343--363. 1993.

J. Kohlas and P.A. Monney.
Propagating Belief Functions Through Constraint Systems,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Int. J. Approximate Reasoning, 5, 433-461. 1991.

J. Kohlas.
Modeling Uncertainty with Belief Functions in Numerical Models,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Europ. J. of Operational Research, 40, 377--388. 1989.

J. Kohlas.
Conditional Belief Structures,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Probability in Engineering and Information Science, 2(4), 415--433. 1988.

J. Kohlas and P.A. Monney and R. Haenni and N. Lehmann.
Model-Based Diagnostics Using Hints,  HTML abstract and PS paper
Institute for Automation and Operations Research, University of Fribourg
Pages 259--266 of: Ch. Fridevaux and J. Kohlas (eds.), Symbolic and Quantitative Approaches to Uncertainty, European Conference ECSQARU'95, Fribourg. Springer. Lecture Notes in Computer Science, no. 946. 1995.

J. Kohlas.
Mathematical Foundations of Evidence Theory.
Institute for Automation and Operations Research, University of Fribourg
Pages 31--64 of: G. Coletti and D. Dubois and R. Scozzafava (eds.), Mathematical Models for Handling Partial Knowledge in Artificial Intelligence. Plenum Press. 1995.

J. Kohlas.
The Mathematical Theory of Evidence -- A Short Introduction.
Institute for Automation and Operations Research, University of Fribourg
Pages 37--53 of: J. Dolezal (eds.), System Modelling and Optimization. Chapman and Hall. 1995.

J. Kohlas and H.W. Brachinger.
Argumentation Systems and Evidence Theory.
Institute for Automation and Operations Research, University of Fribourg
Pages 41--50 of: B. Bouchon-Meunier and R.R. Yager and L.A. Zadeh (eds.), Advances in Intelligent Computing -- IPMU'94, Paris. Springer. Lecture Notes in Computer Science, no. 945 B. 1994.

R. Bissig and J. Kohlas and N. Lehmann.
Fast-division architecture for Dempster-Shafer belief functions.
Institute for Automation and Operations Research, University of Fribourg
D. Gabbay and R. Kruse and A. Nonnengart and H.J. Ohlbach (eds.), Qualitative and Quantitative Practical Reasoning, First International Joint Conference on Qualitative and Quantitative Practical Reasoning; ECSQARU--FAPR'97 . Springer. 1997.

J. Kohlas and P.A. Monney.
Representation of Evidence by Hints.
Institute for Automation and Operations Research, University of Fribourg
Pages 473--492 of: R.R. Yager and J. Kacprzyk and M. Fedrizzi (eds.), Advances in the Dempster-Shafer Theory of Evidence. John Wiley, New York. 1994.

N. Wilson
Chapter 10 : Belief Functions Algorithms
Algorithms for Uncertainty and Defeasible Reasoning

Xu H. (1997)
Valuation Based Systems for Decision Analysis using Belief Functions
International Journal of Decision Support Systems, 20:2, pp.165-184.

Xu H. Hsia Y-T, Smets Ph. (1996)
Transferable Belief Model for Decision Making in Valuation Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, 26:6, pp.698-707.

Xu H. Smets Ph. (1996)
Some Strategies for Explanations in Evidential Reasoning
IEEE Transactions on Systems, Man, and Cybernetics, 26:5, pp.599-607.

Xu H. Smets Ph. (1996)
Reasoning in Evidential Networks with Conditional Belief Functions
International Journal of Approximate Reasoning, 14:2, pp. 155-186.

Xu H. (1995)
Computing Marginals from the Marginal Representation in Markov trees
Artificial Intelligence, 74 pp. 177-189.

Xu H. and Kennes R. (1994)
Steps towards an Efficient Implementation of Dempster-Shafer Theory
Advances in the Dempster-Shafer Theory of Evidence, edited by Yager R. R., Fedrizzi M., and Kacprzyk J. (John Wiley & Sons, Inc.) pp. 153-174.

Xu H. (1993)
An Efficient Tool for Reasoning with Belief Functions Uncertainty in Intelligent Systems
edited by Bouchon-Meunier B., Valverde L. and Yager R. R. (North-Holland: Elsevier Science). pp. 215-224.

Smets Ph., Hsia Y-T, Saffiotti A., Kennes R., Xu H. and Emkehrer E. (1991)
The Transferable Belief Model
Symbolic and Quantitative Approaches to Uncertainty, edited by Kruse R. and Siegel P. (Berlin: Springer Verlag, Lecture Notes in Computer Science No. 458) pp. 91-96.

Xu H. Smets Ph. (1995)
Generating Explanations for Evidential Reasoning
Proc. 11th Uncertainty in Artificial Intelligence, pp. 574-581.

Xu H. (1994)
Computing Marginals from the Marginal Representation in Markov Trees
5th International Conference on Information Proceeding and Management of Uncertainty in Knowledge-Based Systems, pp. 275-280.

Xu H. and Smets Ph. (1994)
Evidential Reasoning with Conditional Belief Functions
Proc. 10th Uncertainty in Artificial Intelligence, edited by Lopez de Mantaras R. and Poole D., pp. 598-605.

Xu H., Hsia Y-T. and Smets Ph. (1993)
A Belief-Function Based Decision Support System
Proc. 9th Uncertainty in Artificial Intelligence, edited by Heckerman D. and Mamdani A., pp. 535-542.

Xu H. (1992)
A Decision Calculus for Belief Functions in Valuation-Based Systems
Proc. 8th Uncertainty in Artificial Intelligence, edited by Dubois D. Wellman M. P. D'Ambrosio B. and Smets Ph., pp. 352-359.

Xu H. (1992)
An Efficient Tool for Reasoning with Belief Functions
4th International Conference on Information Proceeding and Management of Uncertainty in Knowledge-Based Systems, pp. 65-68.

Xu H. (1991)
An Efficient Implementation of the Belief Function Propagation
Proc. 7th Uncertainty in Artificial Intelligence, edited by D'Ambrosio B. D., Smets Ph. and Bonissone P. P., pp. 425-432.