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Practical probabilistic inference

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dc.contributor.advisor Whitman Richards. en_US
dc.contributor.author Morris, Quaid Donald Jozef, 1972- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. en_US
dc.date.accessioned 2006-03-24T18:09:34Z
dc.date.available 2006-03-24T18:09:34Z
dc.date.copyright 2003 en_US
dc.date.issued 2003 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/29989
dc.description Thesis (Ph. D. in Computational Neuroscience)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2003. en_US
dc.description Includes bibliographical references (leaves 157-163). en_US
dc.description.abstract The design and use of expert systems for medical diagnosis remains an attractive goal. One such system, the Quick Medical Reference, Decision Theoretic (QMR-DT), is based on a Bayesian network. This very large-scale network models the appearance and manifestation of disease and has approximately 600 unobservable nodes and 4000 observable nodes that represent, respectively, the presence and measurable manifestation of disease in a patient. Exact inference of posterior distributions over the disease nodes is extremely intractable using generic algorithms. Inference can be made much more efficient by exploiting the QMR-DT's unique structure. Indeed, tailor-made inference algorithms for the QMR-DT efficiently generate exact disease posterior marginals for some diagnostic problems and accurate approximate posteriors for others. In this thesis, I identify a risk with using the QMR-DT disease posteriors for medical diagnosis. Specifically, I show that patients and physicians conspire to preferentially report findings that suggest the presence of disease. Because the QMR-DT does not contain an explicit model of this reporting bias, its disease posteriors may not be useful for diagnosis. Correcting these posteriors requires augmenting the QMR-DT with additional variables and dependencies that model the diagnostic procedure. I introduce the diagnostic QMR-DT (dQMR-DT), a Bayesian network containing both the QMR-DT and a simple model of the diagnostic procedure. Using diagnostic problems sampled from the dQMR-DT, I show the danger of doing diagnosis using disease posteriors from the unaugmented QMR-DT. en_US
dc.description.abstract (cont.) I introduce a new class of approximate inference methods, based on feed-forward neural networks, for both the QMR-DT and the dQMR-DT. I show that these methods, recognition models, generate accurate approximate posteriors on the QMR-DT, on the dQMR-DT, and on a version of the dQMR-DT specified only indirectly through a set of presolved diagnostic problems. en_US
dc.description.statementofresponsibility by Quaid Donald Jozef Morris. en_US
dc.format.extent 163 leaves en_US
dc.format.extent 5612326 bytes
dc.format.extent 5612132 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Brain and Cognitive Sciences. en_US
dc.title Practical probabilistic inference en_US
dc.type Thesis en_US
dc.description.degree Ph.D.in Computational Neuroscience en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. en_US
dc.identifier.oclc 54792349 en_US


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