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dc.contributor.advisorJoshua B. Tenenbaum.en_US
dc.contributor.authorHuggins, Jonathan H. (Jonathan Hunter)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-10-21T16:20:21Z
dc.date.available2014-10-21T16:20:21Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/91033
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.description29en_US
dc.descriptionCataloged from student submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 56-57).en_US
dc.description.abstractSequential Monte Carlo (SMC) methods form a popular class of Bayesian inference algorithms. While originally applied primarily to state-space models, SMC is increasingly being used as a general-purpose Bayesian inference tool. Traditional analyses of SMC algorithms focus on their usage for approximating expectations with respect to the posterior of a Bayesian model. However, these algorithms can also be used to obtain approximate samples from the posterior distribution of interest. We investigate the asymptotic and non-asymptotic properties of SMC from this sampling viewpoint. Let P be a distribution of interest, such as a Bayesian posterior, and let P be a random estimator of P generated by an SMC algorithm. We study ... i.e., the law of a sample drawn from P, as the number of particles tends to infinity. We give convergence rates of the Kullback-Leibler divergence KL ... as well as necessary and sufficient conditions for the resampled version of P to asymptotically dominate the non-resampled version from this KL divergence perspective. Versions of these results are given for both the full joint and the filtering settings. In the filtering case we also provide time-uniform bounds under a natural mixing condition. Our results open up the possibility of extending recent analyses of adaptive SMC algorithms for expectation approximation to the sampling setting.en_US
dc.description.statementofresponsibilityby Jonathan H. Huggins.en_US
dc.format.extent57 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAn information-theoretic analysis of resampling in Sequential Monte Carloen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc892649604en_US


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