next up previous
Next: About this document ... Up: Irrelevance and Independence Relations Previous: Acknowledgements

Bibliography

1
J. O. Berger.
Robust Bayesian analysis: Sensitivity to the prior.
Journal of Statistical Planning and Inference, 25:303-328, 1990.

2
J. S. Breese and K. W. Fertig.
Decision making with interval influence diagrams.
Uncertainty in Artificial Intelligence 6, pages 467-478. Elsevier Science, North-Holland, 1991.

3
A. Cano, J. E. Cano, and S. Moral.
Convex sets of probabilities propagation by simulated annealing.
Fifth IPMU, pages 4-8, July 1994.

4
J. Cano, M. Delgado, and S. Moral.
An axiomatic framework for propagating uncertainty in directed acyclic networks.
International Journal of Approximate Reasoning, 8:253-280, 1993.

5
E. Charniak.
Bayesian networks without tears.
AI Magazine, pages 50-63, Fall 1991.

6
L. Chrisman.
Independence with lower and upper probabilities.
XII Uncertainty in Artificial Intelligence Conference, pages 169-177, 1996.

7
L. Chrisman.
Propagation of 2-monotone lower probabilities on an undirected graph.
XII Uncertainty in Artificial Intelligence Conference, pages 178-186, 1996.

8
F. Cozman.
Independence relations in the robustness analysis of multivariate probabilistic models.
Submitted to the XII Conferência Brasileira de Automática, Brasil, 1998 (available from the author).

9
F. Cozman.
Robustness analysis of Bayesian networks with local convex sets of distributions.
XIII Uncertainty in Artificial Intelligence Conference, 1997.

10
L. de Campos and S. Moral.
Independence concepts for convex sets of probabilities.
XI Uncertainty in Artificial Intelligence, 1995.

11
T. L. Fine.
Lower probability models for uncertainty and nondeterministic processes.
Journal of Statistical Planning and Inference, 20:389-411, 1988.

12
J. Gebhardt and R. Kruse.
Learning possibilistic networks from data.
Fifth International Workshop on Artificial Intelligence and Statistics, 1995.

13
D. Geiger, T. Verma, and J. Pearl.
d-separation: from theorems to algorithms.
Uncertainty in Artificial Intelligence 5, 1990.

14
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.

15
H. E. Kyburg Jr.
Bayesian and non-Bayesian evidential updating.
Artificial Intelligence, 31:271-293, 1987.

16
J. Y. Halpern and R. Fagin.
Two views of belief: Belief as generalized probability and belief as evidence.
Artificial Intelligence, 54:275-317, 1992.

17
T. Ibaraki.
Solving mathematical programming problems with fractional objective functions.
Generalized Concavity in Optimization and Economics, pages 440-472. Academic Press, 1981.

18
F. V. Jensen.
An Introduction to Bayesian Networks.
Springer Verlag, New York, 1996.

19
J. B. Kadane.
Robustness of Bayesian Analyses, volume 4 of Studies in Bayesian econometrics.
Elsevier Science Pub. Co., New York, 1984.

20
I. Levi.
The Enterprise of Knowledge.
The MIT Press, Cambridge, Massachusetts, 1980.

21
J. Pearl.
On probability intervals.
International Journal of Approximate Reasoning, 2:211-216, 1988.

22
J. Pearl.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
Morgan Kauffman, San Mateo, CA, 1988.

23
S. I. Schaible and W. T. Ziemba.
Generalized Concavity in Optimization and Economics.
Academic Press, 1981.

24
T. Seidenfeld, M. J. Schervish, and J. B. Kadane.
A representation of partially ordered preferences.
The Annals of Statistics, 23(6):2168-2217, 1995.

25
P. P. Shenoy and G. Shafer.
Axioms for probability and belief-function propagation.
Uncertainty in Artificial Intelligence 4, pages 169-198. Elsevier Science Publishers, North-Holland, 1990.

26
E. H. Shortliffe and B. G. Buchanan.
Rule-based expert systems.
The Addison-Wesley series in artificial intelligence. Addison-Wesley, Reading, Mass., 1985.

27
B. Tessem.
Interval probability propagation.
International Journal of Approximate Reasoning, 7:95-120, 1992.

28
P. Walley.
Statistical Reasoning with Imprecise Probabilities.
Chapman and Hall, New York, 1991.

29
L. Wasserman.
Recent methodological advances in robust Bayesian inference.
Bayesian Statistics 4, pages 483-502. Oxford University Press, 1992.



Fabio Gagliardi Cozman
1998-07-03