Approximate dynamic programming approach for sequential change diagnosis problem

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buir.advisorDayanık, Savaş
dc.contributor.authorAkbulut, Elif
dc.date.accessioned2016-01-08T18:26:38Z
dc.date.available2016-01-08T18:26:38Z
dc.date.issued2013
dc.departmentDepartment of Industrial Engineeringen_US
dc.descriptionAnkara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 49-51.en_US
dc.description.abstractWe study sequential change diagnosis problem which is the combination of change diagnosis and multi-hypothesis testing problem. One observes a sequence of independent and identically distributed random variables. At a sudden disorder time, the probability distribution of the random variables change. The disorder time and its cause are unavailable to the observer. The problem is to detect this abrupt change in the distribution of the random process as quickly as possible and identify its cause as accurately as possible. Dayanık et al. [Dayanık, Goulding and Poor, Bayesian sequential change diagnosis, Mathematics of Operations Research, vol. 45, pp. 475-496, 2008] reduce the problem to a Markov optimal stopping problem and provide an optimal sequential decision strategy. However, only a small subset of the problems is computationally feasible due to curse of dimensionality. The subject of this thesis is to search for the means to overcome the curse of dimensionality. To this end, we propose several approximate dynamic programming algorithms to solve large change diagnosis problems. On several numerical examples, we compare their performance against the performance of optimal dynamic programming solution.en_US
dc.description.degreeM.S.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:26:38Z (GMT). No. of bitstreams: 1 0006608.pdf: 1310698 bytes, checksum: 767dcba793f4dc0e410215ab9b8f92f6 (MD5)en
dc.description.statementofresponsibilityAkbulut, Elifen_US
dc.format.extentx, 51 leaves, tables, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15911
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSequential change diagnosisen_US
dc.subjectapproximate dynamic programmingen_US
dc.subjectfunction approximationen_US
dc.subject.lccT57.83 .A53 2013en_US
dc.subject.lcshDynamic programming.en_US
dc.subject.lcshMathematical optimization.en_US
dc.subject.lcshControl theory.en_US
dc.subject.lcshApproximation theory.en_US
dc.titleApproximate dynamic programming approach for sequential change diagnosis problemen_US
dc.typeThesisen_US

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