{"type":"ScholarlyArticle","title":"A Method of Computing Generalized Bayesian Probability Values for Expert Systems","authors":[{"name":"Peter C. Cheeseman","slug":"peter-c-cheeseman","given":"Peter C.","family":"Cheeseman","affiliation":"SRI International , 333 Ravenswood Road Menlo Park , California 94025"}],"abstract":"This paper presents a new method for calculating the condi t ional probability of any multi-valued predicate given particular inform ation about the individual case. This calculation is based on the principle of Maximum Entropy (ME), sometimes called the principle of least information, and gives the most unbiased probability estimate given the available evidence. Previous methods for computing maximum entropy values shows that they are either very restrictive in the probabilistic inform at ion (constraints) they can use or combinatorially explosive. Th e computational complexity of the new procedure depends on the inter-connectedness of the constraints, but in practical cases it is small. In addition, the maximum entropy met hod can give a measure of how accurately a calculated con· ditional probability is known.","doi":null,"url":"https://ijcai.org/proceedings/1983/47","pdf":"https://ijcai.org/proceedings/1983/0047.pdf","markdown":"https://ijcai.org/proceedings/1983/47.md","bibtex":"https://ijcai.org/proceedings/1983/bibtex/47","volume":{"slug":"1983","title":"Proceedings of the Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1983","url":"https://ijcai.org/proceedings/1983/","year":1983,"location":"Karlsruhe, West Germany","dates":"","editors":"Alan Bundy","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Expert Systems","track":"Expert Systems","number":47,"pages":{"first":198,"last":201},"keywords":[],"videos":[],"references":[{"position":1,"raw":"Brown, D. T ., \"A Note on Approximations to Discrete Probability Distributions\", Information and Control, 2, pp. 386-392 (1959)","title":"A Note on Approximations to Discrete Probability Distributions","authors":"D T Brown","year":"1959","venue":"Information and Control","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"Chow, C. K., Liu , C. N., \"Approximating Discrete Probability Distributions with Dependence Trees\", IEEE Trans. on Info. Theory, Vol IT-14, No. 3, May (1968)","title":"Approximating Discrete Probability Distributions with Dependence Trees","authors":"C K Chow, C N Liu","year":"1968","venue":"IEEE Trans. on Info. Theory","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"Duda, R. 0 ., Hart, P. E., and Nilsson, N. J ., \"Subjective Bayesian Methods for Rule-Based Inference Systems.\", Proc. AFIPS Nat. Compt. Conf., Vol 47, pp. 1075-1082, (1976)","title":"Subjective Bayesian Methods for Rule-Based Inference Systems","authors":"R 0 Duda, P E Hart, N J Nilsson","year":"1976","venue":"Proc. AFIPS Nat. Compt. Conf","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"Gokhale, D. V. and Kullback, S. \"The Information in Contingency Tables\", New York: Marcel Dekker, (1978)","title":"The Information in Contingency Tables","authors":"D V Gokhale, S Kullback","year":"1978","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"Ireland, C. T . and Kullback, S. \"Contingency Tables with Given Marginals\", Biometrika, Vol. 55, pp. 179-188, March (1968)","title":"Contingency Tables with Given Marginals","authors":"C T Ireland, S Kullback","year":"1968","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"Jaynes, E. T ., \"Prior Probabilities\", IEEE Trans. on Systems Science and Cybernetics, Vol. SSC-4, No. 3, Sept. (1968)","title":"Prior Probabilities","authors":"E T Jaynes","year":"1968","venue":"IEEE Trans. on Systems Science and Cybernetics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"Jaynes, E. T. \"Where Do We Stand on Maximum Entropy\", in \"The Maximum Entropy Formalism\", Levine and Tribus Eds. M.I.T. Press (1979)","title":"Where Do We Stand on Maximum Entropy","authors":"E T Jaynes","year":"1979","venue":"The Maximum Entropy Formalism","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"Konolige, K. Appendix Din \"A Computer-Based Consultant for Mineral Exploration\" SRI Artificial Intelligence report Sept. (1979)","title":"A Computer-Based Consultant for Mineral Exploration","authors":"K Konolige","year":"1979","venue":"SRI Artificial Intelligence report","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"Ku, H. H. and Kullback S., \"Approximating Discrete Probability Distributions\" IEEE Trans. On Information Theory, Vol. IT-15, No. 4, July {1969)","title":"Approximating Discrete Probability Distributions","authors":"H H Ku, S Kullback","year":"1969","venue":"IEEE Trans. On Information Theory","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Lemmer , J . F. and Barth, S. W., \"Efficient Minimum Information Updating for Bayesian Inferencing in Expert Systems\", pp. 424-427, Proc. National Conf. on Art. Intelligence, Pittsburgh, 1982","title":"Efficient Minimum Information Updating for Bayesian Inferencing in Expert Systems","authors":"J F Lemmer, S W Barth","year":"1982","venue":"Proc. National Conf. on Art. Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"Lewis, P . M., \"Approximating Probability Distributions to Reduce Storage Requirements.\" Infomation and Control, 2, pp. 214-225 (1959)","title":"Approximating Probability Distributions to Reduce Storage Requirements","authors":"P M Lewis","year":"1959","venue":"Infomation and Control","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"Pearl, J . \"Distributed Bayesian Processing For Belief Maintenance in Hierarchical Inference Systems\". Report UCLA-ENG-CSir82-ll Univ. of California, Los Angcies (1982)","title":"Distributed Bayesian Processing For Belief Maintenance in Hierarchical Inference Systems","authors":"J Pearl","year":"1982","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"Shafer, G. \"A Mathematical Theory of Evidence\" , Princeton Univ. Press (1979)","title":"A Mathematical Theory of Evidence","authors":"G Shafer","year":"1979","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"Shore, J. E. and Johnson, R. W ., \"Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy\", IEEE Trans. on Info. Theory, Vol. IT-26, No. 1, pp. 26-37, Jan. {1980)","title":"Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy","authors":"J E Shore, R W Johnson","year":"1980","venue":"IEEE Trans. on Info. Theory","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}