References

1

M. Avriel. Advances in Geometric Programming. Plenum Press, New York, 1980.

2

M. Avriel. Nonlinear Programming: Analysis and Methods. Prentice-Hall, Englewood Cliffs, 1980.

3

J. O. Berger. Robust bayesian analysis: Sensitivity to the prior. Journal of Statistical Planning and Inference, 25:303-328, 1990.

4

J. S. Breese and K. W. Fertig. Decision making with interval influence diagrams. UAI 6, pages 467-478. Elsevier Science, North-Holland, 1991.

5

A. Cano, J. E. Cano, and S. Moral. Convex sets of probabilities propagation by simulated annealing. Fifth Int. Conference IPMU, pages 4-8, 1994.

6

J. Cano, M. Delgado, and S. Moral. An axiomatic framework for propagating uncertainty in directed acyclic networks. Int. Journal of Approximate Reasoning, 8:253-280, 1993.

7

L. Chrisman. Independence with lower and upper probabilities. UAI 12, pages 169-177, 1996.

8

L. Chrisman. Propagation of 2-monotone lower probabilities on an undirected graph. UAI 12, pages 178-186, 1996.

9

K. L. Clarkson, K. Mehlhorn, and R. Seidel. Four results on randomized incremental constructions. In STACS, pages 463-474. Springer, 1992.

10

F. Cozman. Robust analysis of bayesian networks with finitely generated convex sets of distributions. CMU-RI-TR96-41, Carnegie Mellon University, December 1996 (available at http://www.cs.cmu.edu/~fgcozman/home.html).

11

F. Cozman. Robustness analysis of bayesian networks with global neighborhoods. CMU-RI-TR96-42, Carnegie Mellon University, December 1996 (available at http://www.cs.cmu.edu/~fgcozman/home.html).

12

R. Dechter. Bucket elimination: A unifying framework for probabilistic inference. UAI 12, pages 211-219, 1996.

13

A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal Royal Statistical Society B, 44:1-38, 1977.

14

L. DeRobertis and J. A. Hartigan. Bayesian inference using intervals of measures. The Annals of Statistics, 9(2):235-244, 1981.

15

T. L. Fine. Lower probability models for uncertainty and nondeterministic processes. Journal of Statistical Planning and Inference, 20:389-411, 1988.

16

F. J. Giron and S. Rios. Quasi-bayesian behaviour: A more realistic approach to decision making? Bayesian Statistics, pages 17-38. University Press, Valencia, Spain, 1980.

17

I. J. Good. Good Thinking. University of Minnesota Press, Minneapolis, 1983.

18

V. Ha and P. Haddawy. Theoretical foundations for abstraction-based probabilistic planning. UAI 12, pages 291-298, 1996.

19

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

20

D. Heckerman, J. Breese, and K. Rommelse. Decision theoretic troubleshooting. Communications of the ACM, 38:49-57, 1995.

21

F. V. Jensen. An Introduction to Bayesian Networks. Springer Verlag, New York, 1996.

22

J. B. Kadane. Robustness of Bayesian Analysis, volume 4 of Studies in Bayesian econometrics. Elsevier Science Pub. Co., New York, 1984.

23

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

24

D. Lambert and G. T. Duncan. Single-parameter inference based on partial prior information. The Canadian Journal of Statistics, 14(4):297-305, 1986.

25

M. Lavine. Sensitivity in bayesian statistics, the prior and the likelihood. Journal of the American Statistical Association, 86(414):396-399, June 1991.

26

J. F. Lemmer and H. E. Kyburg Jr. Conditions for the existence of belief functions corresponding to intervals of belief. Proc. 9th National Conference on Artificial Intelligence, pages 488-493, 1991.

27

I. Levi. The Enterprise of Knowledge. The MIT Press, Cambridge, Massachusetts, 1980.

28

C. F. Manski. Learning and decision making when subjective probabilities have subjective domains. The Annals of Statistics, 9(1):59-65, 1981.

29

J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman, San Mateo, CA, 1988.

30

E. H. Ruspini. The logical foundations of evidential reasoning. Technical Report SRIN408, SRI International, 1987.

31

S. Russell, J. Binder, D. Koller, and K. Kanazawa. Local learning in probabilistic networks with hidden variables. Proc. Fourteenth International Joint Conference on Artificial Intelligence, 1995.

32

S. Schaible. A survey of fractional programming. Generalized Concavity in Optimization and Economics, pages 417-440. Academic Press, 1981.

33

T. Seidenfeld. Outline of a theory of partially ordered preferences. Philosophical Topics, 21(1):173-188, Spring 1993.

34

G. Shafer. A mathematical theory of evidence. Princeton University Press, 1976.

35

C. A. B. Smith. Consistency in statistical inference and decision. Journal Royal Statistical Society B, 23:1-25, 1961.

36

P. Suppes. The measurement of belief. Journal Royal Statistical Society B, 2:160-191, 1974.

37

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

38

L. Wasserman. Recent methodological advances in robust bayesian inference. Bayesian Statistics 4, pages 483-502. Oxford University Press, 1992.

39

J. York. Use of the gibbs sampler in expert systems. Artificial Intelligence, 56:115-130, 1992.

40

N. L. Zhang and D. Poole. Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, pages 301-328, 1996.