Application of bayesian poisson tensor factorization for moving home simulation analysis거주지 이동 시뮬레이션 분석을 위한 베이지안 포아송 텐서 분해 방법론 활용

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dc.contributor.advisorMoon, Il-Chul-
dc.contributor.advisor문일철-
dc.contributor.authorKim, Ki-Ung-
dc.date.accessioned2018-06-20T06:17:49Z-
dc.date.available2018-06-20T06:17:49Z-
dc.date.issued2017-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675218&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/243020-
dc.description학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2017.2,[v, 41 p. :]-
dc.description.abstractToday, by the benefit of globalization, population movements have become much more liberal than before and have played a major role in bringing about socioeconomic changes. We implemented the migration of residence as a representative population movement in the modern country as an agent-based model, and we subdivided and analyzed the simulation results by machine learning method. The moving home simulation is based on the mixture of Schelling's segregation model and Urban Suite model. And we introduce Bayesian Poisson tensor factorization (BPTF) to decompose the agent movement information, which is a 5-way tensor and very sparse. There are simulation results and BPTF results. Under the sudden large change of the input parameter, we could observe that the agents move toward their own gains. And BPTF catches the change time intervals. A more advanced model will help to establish a housing policy by mimicking the real world and detecting more hidden information in advance.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectSchelling’s segregation model-
dc.subjectUrban suite model-
dc.subjectMatrix factorization-
dc.subjectTensor factorization-
dc.subjectBayesian Poisson tensor factorization-
dc.subject쉘링의 분리 모델-
dc.subject도시 스위트 모델-
dc.subject행렬 분해 방법론-
dc.subject텐서 분해 방법론-
dc.subject베이지안 포아송 텐서 분해 방법론-
dc.titleApplication of bayesian poisson tensor factorization for moving home simulation analysis-
dc.title.alternative거주지 이동 시뮬레이션 분석을 위한 베이지안 포아송 텐서 분해 방법론 활용-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :산업및시스템공학과,-
dc.contributor.alternativeauthor김기웅-
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