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Bayesian Nonparametric Modeling based on Species Sampling Models
스피시스 샘플링 모형에 기반한 베이지안 비모수 모형

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dc.contributor.advisor이재용-
dc.contributor.author조성일-
dc.date.accessioned2017-07-14T00:31:09Z-
dc.date.available2017-07-14T00:31:09Z-
dc.date.issued2014-02-
dc.identifier.other000000017430-
dc.identifier.urihttps://hdl.handle.net/10371/121145-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2014. 2. 이재용.-
dc.description.abstractIn this dissertation we propose Bayesian nonparametric models for the inference of spatially varying densities based on mixtures of dependent species sampling models. The species sampling model is a discrete random probability distribution represented as the sum of the random support points with random weights. The spatial dependency is introduced by modeling the weights through the conditional autoregressive model. The proposed models are illustrated in two simulated data sets and show better performance than competitors based on a Dirichlet process or where dependence is not incorporated. The proposed method is also applied to real data sets.-
dc.description.tableofcontentsAbstract i
List of Figures iv
List of Tables viii

1 Introduction and Literature Review 1
1.1 Motivation.............................. 1
1.2 Literature Review.......................... 3
1.2.1 Species Sampling Models.................. 3
1.2.2 Mixture Models with SSMs ................ 7

2 Dependent Species Sampling Models for Spatial Density Estimation 9
2.1 Introduction............................. 9
2.2 Species Sampling Models...................... 13
2.3 Conditional Autoregressive Models ................ 14
2.3.1 Gaussian CAR model.................... 17
2.4 Dependent Species Sampling Models ............... 17
2.4.1 CAR SSMs ......................... 17
2.4.2 Mixtures of CAR SSM ................... 20
2.5 Posterior Computations ...................... 21
2.6 Examples .............................. 26
2.6.1 Simulation studies ..................... 26
2.6.2 Precipitation over Koreanpeninsula. . . . . . . . . . . . 32
2.6.3 Apartment market price .................. 39
2.7 Conclusion.............................. 46

3 Bayesian Regression Models for Seasonal Forecasts of Precipitation over Korea 49
3.1 Introduction............................. 49
3.2 Data................................. 51
3.3 Methodology ............................ 52
3.3.1 Prior to Posterior...................... 52
3.3.2 Choosing one model .................... 56
3.3.3 Computation ........................ 57
3.4 Results and discussion ....................... 59
3.4.1 CPC Merged Analysis of Precipitation data . . . . . . 60
3.4.2 Station-measured precipitation data . . . . . . . . . . . 61
3.5 Concluding remarks......................... 63
Bibliography ..................72
Abstract in Korean .........82
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dc.formatapplication/pdf-
dc.format.extent7584270 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectSpecies sampling model-
dc.subjectDependent species sampling model-
dc.subjectConditional autoregressive model-
dc.subjectSpatial density estimation-
dc.subject.ddc519-
dc.titleBayesian Nonparametric Modeling based on Species Sampling Models-
dc.title.alternative스피시스 샘플링 모형에 기반한 베이지안 비모수 모형-
dc.typeThesis-
dc.description.degreeDoctor-
dc.citation.pagesviii,82-
dc.contributor.affiliation자연과학대학 통계학과-
dc.date.awarded2014-02-
Appears in Collections:
College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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