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David Wingate. Calabrese, Chris, M. Eng. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-03-06T15:39:50Z 2014-03-06T15:39:50Z 2013 2013 http://hdl.handle.net/1721.1/85407 870308346 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 95-97). The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using current approaches because it takes too long for our implementations to converge on useful values. While coming up with more efficient inference algorithms may be the answer, we believe that an alternative approach to solving this complicated problem involves leveraging the computation power of multiple processors or machines with existing inference algorithms. This thesis describes the design and implementation of such a system by combining a variational inference implementation (Variational Message Passing) with a high-level distributed framework (Graphlab) and demonstrates that inference is performed faster on a few large graphical models when using this system. by Chris Calabrese. M.Eng. 97 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Distributed inference : combining variational inference with distributed computing Combining variational inference with distributed computing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="602a7183-cf55-4ce4-8dc1-659fe0ef562e"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Distributed inference : combining variational inference with distributed computing</Title> <Subtitle>Combining variational inference with distributed computing</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Calabrese, Chris, M. Eng. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using current approaches because it takes too long for our implementations to converge on useful values. While coming up with more efficient inference algorithms may be the answer, we believe that an alternative approach to solving this complicated problem involves leveraging the computation power of multiple processors or machines with existing inference algorithms. This thesis describes the design and implementation of such a system by combining a variational inference implementation (Variational Message Passing) with a high-level distributed framework (Graphlab) and demonstrates that inference is performed faster on a few large graphical models when using this system.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>