Representation dependence in probabilistic inference

Journal of Artificial Intelligence Research

Volumn 21, Issue , 2004, Pages 319-356

  Halpern, Joseph Y ^a   Koller, Daphne ^b  

^a School of Operations Research and Information Engineering   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY; PROBABILISTIC LOGICS; PROBLEM SOLVING;

NON-DEDUCTIVE REASONING SYSTEMS; PROBABILISTIC INFERENCE; RELATIVE ENTROPY; REPRESENTATION-INDEPENDENT PROBABILISTIC INFERENCE;

INFERENCE ENGINES;

EID: 23944517877     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1292     Document Type: Review

References (27)

1. Bacchus, F. (1990). Representing and Reasoning with Probabilistic Knowledge. MIT Press, Cambridge, Mass.
2. Bacchus, F., Grove, A. J., Halpern, J. Y., & Koller, D. (1996). From statistical knowledge bases to degrees of belief. Artificial Intelligence, 87(1-2), 75-143.
3. Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. Wiley, New York.
4. Enderton, H. B. (1972). A Mathematical Introduction to Logic. Academic Press, New York.
5. Giunchiglia, F., & Walsh, T. (1992). A theory of abstraction. Artificial Intelligence, 56(2-3), 323-390.
6. Goldszmidt, M., Morris, P., & Pearl, J. (1993). A maximum entropy approach to nonmonotonic reasoning. IEEE Transactions of Pattern Analysis and Machine Intelligence, 15(3), 220-232.
7. Halpern, J. Y., & Koller, D. (1995). Representation dependence in probabilistic inference. In Proc. Fourteenth International Joint Conference on Artificial Intelligence (IJCAI '95), pp. 1853-1860.
8. Horn, A., & Tarski, A. (1948). Measures in Boolean algebras. Transactions of the AMS, 64(1), 467-497.
9. Jaeger, M. (1996). Representation independence of nonmonotonic inference relations. In Principles of Knowledge Representation and Reasoning: Proc. Fifth International Conference (KR '96), pp. 461-472.
10. Jaynes, E. T. (1968). Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, SSC-4, 227-241.
11. Jaynes, E. T. (1978). Where do we stand on maximum entropy?. In Levine, R. D., & Tribus, M. (Eds.), The Maximum Entropy Formalism, pp. 15-118. MIT Press, Cambridge, Mass.
12. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge/New York.
13. Kass, R. E., & Wasserman, L. (1993). Formal rules for selecting prior distributions: A review and annotated bibliography. Tech. rep. Technical Report #583, Dept. of Statistics, Carnegie Mellon University.
14. Keisler, J., & Tarski, A. (1964). From accessible to inaccessible cardinals. Fundamenta Mathematics 53, 225-308.
15. Kraus, S., Lehmann, D., & Magidor, M. (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44, 167-207.
16. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 76-86.
17. Lehmann, D., & Magidor, M. (1992). What does a conditional knowledge base entail?. Artificial Intelligence, 55, 1-60.
18. Nayak, P. P., & Levy, A. Y. (1995). A semantic theory of abstractions. In Proc. Fourteenth International Joint Conference on Artificial Intelligence (IJCAI '95), pp. 196-203.
19. Paris, J. B. (1994). The Uncertain Reasoner's Companion. Cambridge University Press, Cambridge, U.K.
20. Paris, J., & Vencovská, A. (1990). A note on the inevitability of maximum entropy. International Journal of Approximate Reasoning, 4(3), 183-224.
21. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco.
22. Salmon, W. (1961). Vindication of induction. In Feigl, H., & Maxwell, G. (Eds.), Current Issues in the Philosophy of Science, pp. 245-264. Holt, Rinehart, and Winston, New York.
23. Salmon, W. (1963). On vindicating induction. In Kyburg, H. E., & Nagel, E. (Eds.), Induction: Some Current Issues, pp. 27-54. Wesleyan University Press, Middletown, Conn.
24. Seidenfeld, T. (1987). Entropy and uncertainty. In MacNeill, I. B., & Umphrey, G. J. (Eds.), Foundations of Statistical Inferences, pp. 259-287. Reidel, Dordrecht, Netherlands.
25. Shore, J. E., & Johnson, R. W. (1980). Axiomatic derivation of the principle of maximum entropy and the principle of minimimum cross-entropy. IEEE Transactions on Information Theory, IT-26(1), 26-37.
26. Ulam, S. (1930). Zur masstheorie in der allgemeinen mengenlehre. Fundamenta Mathematicae, 16, 140-150.
27. Walley, P. (1996). Inferences from multinomial data: learning about a bag of marbles. Journal of the Royal Statistical Society, Series B, 58(1), 3-34. Discussion of the paper by various commentators appears on pp. 34-57.