• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.413-416
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• 2001

우리의 주변에는 복잡한 소프트웨어 시스템(System)들로 둘러 쌓여 있으며 이러한 시스템의 혜택을 받는 일이 커짐에 따라 소프트웨어 신뢰성의 역할은 점차 커지게 되었다. 고장 시스템이 복잡해지면 고장의 원인이 하나의 수명분포에 의해서만 일어나지 않고 여러 원인이 혼합되어 발생 할 수 있다. 이러한 복잡한 시스템에 의한 우도함수가 적분하기 난해하므로 반복표본을 이용하는 깁스 알고리즘이 제안되었다.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.73-84
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• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.477-479
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• 2011

본 논문에서는 1차원 은닉 마코프 모델을 2차원으로 확장하기 위하여 노드들의 마코프 특성이 인과적인 관계를 갖는 마코프 메쉬 모델을 이용하여 완전한 2차원 HMM의 구조를 갖는 모델을 제안한다. 마코프메쉬 모델은 이웃시스템을 통하여 이전의 시점을 정의하고, 인과적인 관계를 통하여 전이확률의 계산을 가능하게 한다. 또한 영상의 최적의 분할을 위하여 계층적 디리슐레 과정을 사전분포로 두어 고정된 상태의 수가 아닌 무한의 상태 수를 갖는 2차원 HMM을 제안한다. HDP로 정의된 사전분포와 관측된 표본 자료의 정보를 갖는 우도함수를 결합한 사후분포의 베이스 추정은 깁스샘플링 알고리즘을 이용하여 계산된다.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.119-128
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• 2002

In this paper, we consider the HB estimators of small area means with repeated survey. mao and Yu(1994) considered small area model with repeated survey data and proposed empirical best linear unbiased estimators. We propose a hierachical Bayes version of Rao and Yu by assigning prior distributions for unknown hyperparameters. We illustrate our HB estimator using very popular data in small area problem and then compare the results with the estimator of Census Bureau and other estimators previously proposed.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.153-164
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• 1996

We discussed estimation of parameters using Gibbs sampler under order restriction on the parameters. Two well-knwon probability models, ordered exponential family and binomial distribution, are considered. We derived full conditional distributions(FCD) and also used one-for-one sampling algorithm to sample from the FCD's under order restrictions. Finally through two real data sets we compared three kinds of estimators; isotonic regression estimator, isotonic Bayesian estimator and the estimator using Gibbs sampler.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2060-2071
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• 1999

The complicate software failure system is defined to the superposition of the points of failure from several component point process. Because the likelihood function is difficulty in computing, we consider Gibbs sampler using iteration sampling based method. For each observed failure epoch, we applied to latent variables that indicates with component of the superposition mode. For model selection, we explored the posterior Bayesian criterion and the sum of relative errors for the comparison simple pattern with superposition model. A numerical example with NHPP simulated data set applies the thinning method proposed by Lewis and Shedler[25] is given, we consider Goel-Okumoto model and Weibull model with GOS, inference of parameter is studied. Using the posterior Bayesian criterion and the sum of relative errors, as we would expect, the superposition model is best on model under diffuse priors.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.57-68
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• 2017

Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1069-1076
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• 2014

Rainfall weather patterns have changed due to global warming and sudden heavy rainfalls have become more frequent. Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in Seoul based on data from 1973 to 2008. We use several priors including Jeffrey's noninformative prior and Gibbs sampling method to derive Bayesian posterior predictive distributions. The probability of heavy rainfall has increased over the last ten years based on estimated posterior predictive distribution.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1039-1047
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• 2014

An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.389-397
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both Gumbel distribution and trend analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.