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References
- Berger1985
-
Berger, J. O.
1985.
Statistical Decision Theory and Bayesian Analysis.
Springer-Verlag.
- Berger1990
-
Berger, J. O.
1990.
Robust bayesian analysis: Sensitivity to the prior.
Journal of Statistical Planning and Inference 25:303-328.
- Breese & Fertig1991
-
Breese, J. S., and Fertig, K. W.
1991.
Decision making with interval influence diagrams.
In Bonissone, P. P.; Henrion, M.; Kanal, L. N.; and Lemmer, J. F.,
eds., Uncertainty in Artificial Intelligence 6. North-Holland: Elsevier
Science.
467-478.
- Buntine1994
-
Buntine, W. L.
1994.
Operations for learning with graphical models.
Journal of Artificial Intelligence Research 2:159-225.
- Cannings & Thompson1981
-
Cannings, C., and Thompson, E. A.
1981.
Genealogical and Genetic Structure.
Cambridge: Cambridge University Press.
- Cano, Delgado, & Moral1993
-
Cano, J.; Delgado, M.; and Moral, S.
1993.
An axiomatic framework for propagating uncertainty in directed
acyclic networks.
International Journal of Approximate Reasoning 8:253-280.
- Chrisman1996a
-
Chrisman, L.
1996a.
Independence with lower and upper probabilities.
Proc. Twelfth Conference Uncertainty in Artificial Intelligence
169-177.
- Chrisman1996b
-
Chrisman, L.
1996b.
Propagation of 2-monotone lower probabilities on an undirected graph.
Proc. Twelfth Conference Uncertainty in Artificial Intelligence
178-186.
- Dechter1996
-
Dechter, R.
1996.
Bucket elimination: A unifying framework for probabilistic inference.
Proc. of the Twelfth Conference Uncertainty in Artificial
Intelligence 211-219.
- Dempster, Laird, & Rubin1977
-
Dempster, A. P.; Laird, N. M.; and Rubin, D. B.
1977.
Maximum likelihood from incomplete data via the EM algorithm.
Journal Royal Statistical Society B 44:1-38.
- DeRobertis & Hartigan1981
-
DeRobertis, L., and Hartigan, J. A.
1981.
Bayesian inference using intervals of measures.
The Annals of Statistics 9(2):235-244.
- Fine1988
-
Fine, T. L.
1988.
Lower probability models for uncertainty and nondeterministic
processes.
Journal of Statistical Planning and Inference 20:389-411.
- Geman & Geman84
-
Geman, S., and Geman, D.
84.
Stochastic relaxation, gibbs distribution and the bayesian
restoration of images.
IEEE Transactios on Patter Analysis and Machine Intelligence
PAMI-6(6):721-741.
- Giron & Rios1980
-
Giron, F. J., and Rios, S.
1980.
Quasi-bayesian behaviour: A more realistic approach to decision
making?
In Bernardo, J. M.; DeGroot, J. H.; Lindley, D. V.; and Smith, A.
F. M., eds., Bayesian Statistics. Valencia, Spain: University Press.
17-38.
- Good1983
-
Good, I. J.
1983.
Good Thinking: The Foundations of Probability and its
Applications.
Minneapolis: University of Minnesota Press.
- Ha & Haddawy1996
-
Ha, V., and Haddawy, P.
1996.
Theoretical foundations for abstraction-based probabilistic planning.
Proc. Twelfth Conference Uncertainty in Artificial Intelligence
291-298.
- Halpern & Fagin1992
-
Halpern, J. Y., and Fagin, R.
1992.
Two views of belief: Belief as generalized probability and belief as
evidence.
Artificial Intelligence 54:275-317.
- Heckerman1990
-
Heckerman, D.
1990.
An empirical comparison of three inference methods.
In Shachter, R. D.; Kanal, L. N.; and Lemmer, J. F., eds.,
Uncertainty in Artificial Intelligence 4. North-Holland: Elsevier Science
Publishers.
283-303.
- Huber1980
-
Huber, P. J.
1980.
Robust Statistics.
New York: Wiley.
- Jensen & Jensen1994
-
Jensen, F., and Jensen, F. V.
1994.
From influence diagrams to junction trees.
Technical Report R94-2013, Aalborg University.
- Jensen1996
-
Jensen, F. V.
1996.
An Introduction to Bayesian Networks.
New York: Springer Verlag.
- Kadane1984
-
Kadane, J. B.
1984.
Robustness of Bayesian Analyses, volume 4 of Studies in
Bayesian econometrics.
New York: Elsevier Science Pub. Co.
- Kennes1992
-
Kennes, R.
1992.
Computational aspects of the mŏbius transformation of graphs.
IEEE Transactions on Systems, Man and Cybernetics
22(2):201-223.
- Kyburg Jr.1987
-
Kyburg Jr., H. E.
1987.
Bayesian and non-bayesian evidential updating.
Artificial Intelligence 31:271-293.
- Lambert & Duncan1986
-
Lambert, D., and Duncan, G. T.
1986.
Single-parameter inference based on partial prior information.
The Canadian Journal of Statistics 14(4):297-305.
- Lavine1991
-
Lavine, M.
1991.
Sensitivity in bayesian statistics, the prior and the likelihood.
Journal of the American Statistical Association
86(414):396-399.
- Levi1980
-
Levi, I.
1980.
The Enterprise of Knowledge.
Cambridge, Massachusetts: The MIT Press.
- Manski1981
-
Manski, C. F.
1981.
Learning and decision making when subjective probabilities have
subjective domains.
The Annals of Statistics 9(1):59-65.
- Pearl1988
-
Pearl, J.
1988.
Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference.
San Mateo, CA: Morgan Kauffman.
- Press et al.
1992
-
Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; and Flannery, B. P.
1992.
Numerical Recipes in C.
Cambridgeshire: Cambridge University Press.
- Ruspini1987
-
Ruspini, E. H.
1987.
The logical foundations of evidential reasoning.
Technical Report SRIN408, SRI International.
- Russell et al.
1995
-
Russell, S.; Binder, J.; Koller, D.; and Kanazawa, K.
1995.
Local learning in probabilistic networks with hidden variables.
Proc. Fourteenth International Joint Conference on Artificial
Intelligence.
- Seidenfeld &
Schervish1990
-
Seidenfeld, T., and Schervish, M.
1990.
Two perspectives on consensus for (bayesian) inference and decisions.
IEEE Transactions on Systems, Man and Cybernetics 20(1).
- Seidenfeld1993
-
Seidenfeld, T.
1993.
Outline of a theory of partially ordered preferences.
Philosophical Topics 21(1):173-188.
- Shafer1976
-
Shafer, G.
1976.
A mathematical theory of evidence.
Princeton University Press.
- Shafer1987
-
Shafer, G.
1987.
Probability judgment in artificial intelligence and expert systems.
Statistical Science 2(1):3-44.
- Shenoy & Shafer1990
-
Shenoy, P. P., and Shafer, G.
1990.
Axioms for probability and belief-function propagation.
In Shachter, R. D.; Levitt, T. S.; Kanal, L. N.; and Lemmer, J. F.,
eds., Uncertainty in Artificial Intelligence 4. North-Holland: Elsevier
Science Publishers.
169-198.
- Smith1961
-
Smith, C. A. B.
1961.
Consistency in statistical inference and decision.
Journal Royal Statistical Society B 23:1-25.
- Suppes1974
-
Suppes, P.
1974.
The measurement of belief.
Journal Royal Statistical Society B 2:160-191.
- Tessem1992
-
Tessem, B.
1992.
Interval probability propagation.
International Journal of Approximate Reasoning 7:95-120.
- Walley1991
-
Walley, P.
1991.
Statistical Reasoning with Imprecise Probabilities.
New York: Chapman and Hall.
- Wasserman & Kadane1992
-
Wasserman, L., and Kadane, J. B.
1992.
Computing bounds on expectations.
Journal of the American Statistical Association
87(418):516-522.
- Wasserman1990
-
Wasserman, L. A.
1990.
Prior envelopes based on belief functions.
The Annals of Statistics 18(1):454-464.
- Wasserman1992a
-
Wasserman, L.
1992a.
Invariance properties of density ratio priors.
The Annals of Statistics 20(4):2177-2182.
- Wasserman1992b
-
Wasserman, L.
1992b.
Recent methodological advances in robust bayesian inference.
In Bernardo, J. M.; Berger, J. O.; Dawid, A. P.; and Smith, A. F. M.,
eds., Bayesian Statistics 4. Oxford University Press.
483-502.
- Zhang & Poole1996
-
Zhang, N. L., and Poole, D.
1996.
Exploiting causal independence in Bayesian network inference.
Journal of Artificial Intelligence Research 301-328.
© Fabio Cozman[Send Mail?]
Tue Jan 21 15:59:56 EST 1997