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UNCERTAINTY QUANTIFICATION WITH alpha-STABLE-PROCESS MODELS
Tuo, Rui
2018-04-01
Source PublicationSTATISTICA SINICA
ISSN1017-0405
Volume28Issue:2Pages:553-576
AbstractIn this article we consider using a class of alpha-stable processes, which can be regarded as generalizations of the Gaussian processes, as the surrogate models for uncertainty quantification. We introduce a class of alpha-stable processes, whose finite-dimensional distributions can be represented using independent stable random variables. This representation allows for Bayesian inference for the proposed statistical model. We can obtain the posterior distributions for the untried points as well as the model parameters through an MCMC algorithm. The computation for the representation requires some geometrical information given by the design points. We propose an efficient algorithm to solve this computational geometry problem. Two examples are given to illustrate the proposed method and its potential advantages.
KeywordComputer experiments kriging Levy processes stable distributions
Language英语
Funding ProjectNSFC[11501551] ; NSFC[11271355] ; NSFC[11671386] ; National Center for Mathematics and Interdisciplinary Sciences, CAS ; NSF[DMS 1007574]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000450211500002
PublisherSTATISTICA SINICA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31743
Collection系统科学研究所
AffiliationChinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Tuo, Rui. UNCERTAINTY QUANTIFICATION WITH alpha-STABLE-PROCESS MODELS[J]. STATISTICA SINICA,2018,28(2):553-576.
APA Tuo, Rui.(2018).UNCERTAINTY QUANTIFICATION WITH alpha-STABLE-PROCESS MODELS.STATISTICA SINICA,28(2),553-576.
MLA Tuo, Rui."UNCERTAINTY QUANTIFICATION WITH alpha-STABLE-PROCESS MODELS".STATISTICA SINICA 28.2(2018):553-576.
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