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dc.contributor.authorAnhTuanBuien_US
dc.date.accessioned2014-12-01T11:47:43Z-
dc.date.available2014-12-01T11:47:43Z-
dc.date.issued2012en_US
dc.identifier.otherOAK-2014-00828en_US
dc.identifier.urihttp://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001215434en_US
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/1330-
dc.descriptionMasteren_US
dc.description.abstractCausal structure learning algorithms construct Bayesian networks from observational data. Constraint-based algorithms use conditional independence tests to detect relationship among variables. Using non-interventional data, existing constraint-based algorithms may return I-equivalent partially directed acyclic graphs. In worst case, these algorithms may suffer from exponentially complexity. Some recent algorithms utilize Markov blanket approach to deal with this problem.However, these algorithms do not fully exploit graphical properties of Bayesian networks and they require many redundant tests that cause bothslower speed and lower accuracy. This thesis introduces some ideas to exploit such properties to enhance causal structure learning performance.Numerical experiments on five benchmarking networks show that the proposed algorithm outperforms recently developed algorithms. Furthermore, theoretical study is also discussed to support the correctness of the proposed method.en_US
dc.languageengen_US
dc.publisher포항공과대학교en_US
dc.rightsBY_NC_NDen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/kren_US
dc.titleFast Causal Structure Learning Using Markov Blanket Decompositionen_US
dc.typeThesisen_US
dc.contributor.college일반대학원 기계산업공학부en_US
dc.date.degree2012- 2en_US
dc.contributor.departmentPohang University of Science and Technology, Division of Mechanical and Industrial Engineeringen_US
dc.type.docTypeThesis-

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