• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.40.2-40.2
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• 2019

허블 텐션이란 허블우주망원경으로 관측한 허블상수 값과 플랑크 위성으로 측정한 허블상수 값이 일치하지 않는 문제를 일컬으며 현재 우주론에서 주목 받는 이슈 중 하나이다. 밀도가 작은 지역에선 약한 중력으로 공간의 팽창이 빠르고, 반대로 밀도가 큰 지역에서는 팽창이 느리다. 만약, 우리 근처에서 상대적으로 낮은 밀도 때문에 팽창 속도의 차이가 생긴다면 허블 텐션의 원인을 쉽게 설명할 수 있다. 이 문제를 구체적으로 다루기 위해, 우리는 우주 상수를 고려한 아인슈타인 중력의 구형 우주론 풀이인 Lambda-Lemaître-Tolman (ΛLT) 모형을 사용하였다. 우리로부터 먼 현상은 기존의 ΛCDM(Λ cold dark matter) 모형으로, 가까운 현상은 국소적인 LT 모형으로 기술함으로써 허블 텐션 문제를 해결하고자 하였다. 또한, 마코프 체인 몬테 칼로 (MCMC) 방법을 적용하여 천문 관측 결과를 잘 맞추는 ΛLT 모형의 변수들을 탐색하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2
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• pp.179-194
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• 2020

A Bayesian approach was introduced to improve the belief of prior distributions of input parameters for the probabilistic safety assessment of radioactive waste repository. A GoldSim-based module was developed using the Markov chain Monte Carlo algorithm and implemented through GSTSPA (GoldSim Total System Performance Assessment), a GoldSim template for generic/site-specific safety assessment of the radioactive repository system. In this study, sequential Bayesian updating of prior distributions was comprehensively explained and used as a basis to conduct a reliable safety assessment of the repository. The prior distribution to three sequential posterior distributions for several selected parameters associated with nuclide transport in the fractured rock medium was updated with assumed likelihood functions. The process was demonstrated through a probabilistic safety assessment of the conceptual repository for illustrative purposes. Through this study, it was shown that insufficient observed data could enhance the belief of prior distributions for input parameter values commonly available, which are usually uncertain. This is particularly applicable for nuclide behavior in and around the repository system, which typically exhibited a long time span and wide modeling domain.