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dc.creatorJefferys, W. H.en
dc.date.accessioned2015-04-16T14:47:45Zen
dc.date.available2015-04-16T14:47:45Zen
dc.date.issued2007-11en
dc.identifier.citationWilliam H. Jefferys. AIP Conference Proceedings 954, 85 (Nov., 2007); doi: 10.1063/1.2821303en
dc.identifier.issn0094-243Xen
dc.identifier.issn978-0-7354-0468-7en
dc.identifier.urihttp://hdl.handle.net/2152/29425en
dc.description.abstractIn a widely-cited paper, Glymour (Theory and Evidence, Princeton, N. J.: Princeton University Press, 1980, pp. 63-93) claims to show that Bayesians cannot team from old data. His argument contains an elementary error. I explain exactly where Glymour went wrong, and how the problem should be handled correctly. When the problem is fixed, it is seen that Bayesians, just like logicians, can indeed learn from old data.en
dc.language.isoEnglishen
dc.rightsAdministrative deposit of works to UT Digital Repository: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access or the publisher allows a PDF version of the article to be freely posted online. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en
dc.subjectlogicen
dc.subjectprobability theoryen
dc.subjectbayesian inferenceen
dc.subjectproblem of old dataen
dc.subjectmathematics, applieden
dc.subjectphysics, applieden
dc.titleBayesians Can Learn From Old Dataen
dc.typeArticleen
dc.description.departmentAstronomyen
dc.identifier.doi10.1063/1.2821303en
dc.contributor.utaustinauthorJefferys, William H.en
dc.relation.ispartofserialBayesian Inference and Maximum Entropy Methods in Science and Engineeringen_US


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