Deep gaussian process models for integrating multifidelity experiments having non-stationary relationships = 심층 가우시안 프로세스를 이용한 비정상성 다중 정밀도 통합모델

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dc.contributor.advisorKim, Heeyoung-
dc.contributor.advisor김희영-
dc.contributor.authorKo, Jongwoo-
dc.date.accessioned2021-05-12T19:35:08Z-
dc.date.available2021-05-12T19:35:08Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910093&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/283921-
dc.description학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2020.2,[iii, 23 p. :]-
dc.description.abstractThe problem of integrating multifidelity data has been extensively studied, as an integrated analysis can produce better results than analyzing each type separately. A popular approach is to use a linear autoregressive model with location and scale adjustment parameters. These parameters are usually modeled using stationary Gaussian processes. However, the stationarity assumption may not be appropriate in practice. To introduce nonstationarity for more flexibility, we propose a new integration model that is based on deep Gaussian processes, which can capture nonstationarity by successive warping of latent variables through multiple layers of Gaussian processes. For inference of the proposed model, we derive a doubly stochastic variational inference algorithm. We validate the proposed model using simulated and real-data examples.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectComputer experiments▼aDeep Gaussian process▼aDoubly stochastic variational inference▼aNonstationary-
dc.subject컴퓨터 실험▼a심층 가우시안 프로세스▼a이중확률 변분추론▼a비정상성-
dc.titleDeep gaussian process models for integrating multifidelity experiments having non-stationary relationships = 심층 가우시안 프로세스를 이용한 비정상성 다중 정밀도 통합모델-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :산업및시스템공학과,-
dc.contributor.alternativeauthor고종우-
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