• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.379-391
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• 2001

본 연구에서는 관심거리가 되고 있는 마코프인쇄 몬테칼로(Markov Chain Monte Carlo, MCMC)방법에 근거한 공간 확률난수 (spatial random variate)생성법과 깁스표본추출법(Gibbs sampling)에 의한 베이지안 분석 방법에 대한 기술적 사항들에 관하여 검토하였다. 먼저 기본적인 확률난수 생성법과 관련된 사항을 살펴보고, 다음으로 조건부명시법(conditional specification)을 이용한 공간 확률난수 생성법을 예를 들어 살펴보기로한다. 다음으로는 이렇게 생성된 공간자료를 분석하기 위하여 깁스표본추출법을 이용한 베이지안 사후분포를 구하는 방법을 살펴보았다.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.427-438
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• 2013

Based on progressive Type II censored sampling which is an important method to obtain failure data in a lifetime study, we suggest a very general form of Bayesian prediction bounds from two parameters exponentiated Weibull distribution using the proper general prior density. For this, Markov chain Monte Carlo approach is considered and we also provide a simulation study.

• Current Optics and Photonics
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• v.5 no.6
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• pp.597-605
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• 2021

An extended Fourier modal method (FMM) for optical dipole radiation in three-dimensional photonic structures is proposed. The core elements of the proposed FMM are the stable bidirectional scattering-matrix algorithm for modeling internal optical emission, and a novel optical-dipole-source model that prevents numerical errors induced by the Gibbs phenomenon. Through the proposed scheme, the FMM is extended to model a wide range of source-embedded photonic structures.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.144-154
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• 2001

협력적 여과 시스템은 사용자가 검색하고 읽었던 웹문서를 기반으로 사용자 군집을 생성하여 웹문서의 정확한 추천을 가능하게 한다. 이러한 목적으로 설계된 다양한 알고리즘이 있으나 속도가 느리거나 정확도가 낮다는 등의 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위하여 협력적 여과 시스템을 위한 효과적인 사용자 군집 알고리즘인 CUG알고리즘은 사용자 군집을 생성하기 위해 Apriori 알고리즘, Native Bayes 알고리즘을 이용한다. Apriori 알고리즘은 연관 단어 지식 베이스를 구축하고, Native Bayes 알고리즘은 구축된 연관 단어 지식 베이스에 가중치를 추가하며, 사용자가 검색하여 읽은 웹문서를 클래스별로 분류한다. CUG 알고리즘은 분류된 웹문서를 기반으로 하여 사용자 군집을 만든다. 이러한 방법으로 설계된 CUG 알고리즘은 사용자들이 사용할 문서를 미리 검색하여 저장함에 의해 정보검색의 효율성을 향상시키는데 사용될 수 있다. 본 논문에서 설계한 CUG 알고리즘의 선능을 평가하기 위하여 기존의 K-means 방법과 Gibbs샘플링 방법에 의한 군집과 비교한다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.225-235
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• 2009

Lattice process models are used to explain phase transitions in statistical mechanics, a branch of physics. The Ising model, a specific form of lattice process model, was proposed by Ising in 1925. Since then, variants of the Ising model such as the Potts model and the unitary cell model have been proposed. Like the Ising model, it is believed that the more general models exhibit phase transitions on the critical surface, which is based on the mathematical equation. In statistical sense, phase transitions can be simulated through Markov Chain Monte Carlo (MCMC). We applied Swendsen-Wang algorithm, a block Gibbs algorithm, to a general lattice process models and we simulate phase transition phenomena of the unitary cell model.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.705-720
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• 2012

In this paper, we introduce a hierarchical Bayesian model to simultaneously estimate the thresholds of each 6 cities. It was noted in the literature there was a dramatic increases in the number of deaths if the mean temperature passes a certain value (that we call a threshold). We estimate the difference of mortality before and after the threshold. For the hierarchical Bayesian analysis, some proper prior distribution of parameters and hyper-parameters are assumed. By combining the Gibbs and Metropolis-Hastings algorithm, we constructed a Markov chain Monte Carlo algorithm and the posterior inference was based on the posterior sample. The analysis shows that the estimates of the threshold are located at $25^{\circ}C{\sim}29^{\circ}C$ and the mortality around the threshold changes from -1% to 2~13%.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.23-31
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• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.627-640
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• 2017

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.2
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• pp.16-22
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• 2001

In this paper we mose a new scheme for recognition of deformed letters by extracting feature vectors based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are comprised of 2-D conditional moments which are invariant under translation, rotation, and scale of an image. The Algorithm for pattern recognition of deformed letters contains two parts: the extraction of feature vector and the recognition process. (i) We extract feature vector which consists of an improved 2-D conditional moments on the basis of estimated conditional Gibbs distribution for an image. (ii) In the recognition phase, the minimization of the discrimination cost function for a deformed letters determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, recognition experiments with a generated document was conducted. on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 96%.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.665-672
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• 2021

The paper examines the impact of shadow economy and corruption, along with public expenditure, trade openness, foreign direct investment (FDI), inflation, and tax revenue on the economic growth of the BRICS countries. Data were collected from the World Bank, Transparency International, and Heritage Foundation over the 1991-2017 period. The Bayesian linear regression method is used to examine whether shadow economy, corruption and other indicators affect the economic growth of countries studied. This paper applies the normal prior suggested by Lemoine (2019) while the posterior distribution is simulated using Monte Carlo Markov Chain (MCMC) technique through the Gibbs sampling algorithm. The results indicate that public expenditure and trade openness can enhance the BRICS countries' economic growth, with the positive impact probability of 75.69% and 67.11%, respectively. Also, FDI, inflation, and tax revenue positively affect this growth, though the probability of positive effect is ambiguous, ranging from 51.13% to 56.36%. Further, the research's major finding is that shadow economy and control of corruption have a positive effect on the economic growth of the BRICS countries. Nevertheless, the posterior probabilities of these two factors are 62.23% and 65.25%, respectively. This result suggests that their positive effect probability is not high.