Constraints as data: A new perspective on inferring probabilities

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2001, Pages 755-760

  Jaeger, Manfred ^a  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY MAXIMIZATION; LINEAR CONSTRAINTS; NEW APPROACHES; ROBUSTNESS PROPERTIES; STATISTICAL INFERENCE;

ARTIFICIAL INTELLIGENCE; ENTROPY; PROBABILITY DISTRIBUTIONS;

STATISTICAL METHODS;

EID: 8344221410     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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3. M. J. Druzdzel and L. C. van der Gaag. Elicitation of probabilities for belief networks: Combining qualitative and quantitative information. In Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), pages 141-148, Montreal, Quebec, Canada, 1995.
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7. J. F. Lemmer and S. W. Barth. Efficient minimum information updating for Bayesian inferencing in expert systems. In Proceedings of AAAI 82, pages 424-427, 1982.
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9. W. Rödder and C.-H. Meyer. Coherent knowledge processing at maximum entropy by SPIRIT. In Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 470-476, Portland, Oregon, 1996.
10. J.E. Shore and R.W. Johnson. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Transactions on Information Theory, IT-26(1):26-37, 1980.