next up previous
Up: Robustness Analysis of Bayesian Previous: Acknowledgements

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