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

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Authors
조성일
Advisor
이재용
Major
자연과학대학 통계학과
Issue Date
2014-02
Publisher
서울대학교 대학원
Keywords
Species sampling modelDependent species sampling modelConditional autoregressive modelSpatial density estimation
Description
학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2014. 2. 이재용.
Abstract
In 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.
Language
English
URI
http://hdl.handle.net/10371/121145
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Theses (Ph.D. / Sc.D._통계학과)
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