• Journal of Korean Society for Quality Management
• /
• 제27권1호
• /
• pp.80-90
• /
• 1999

In this paper, reliability estimation using Gibbs sampler is considered for the mixture model with Gamma family, Gibbs sampler is derived to compute the features for the posterior distribution. By simulation study, the maximum likelihood estimator and the Gibbs estimator are obtained. A numerical study with a simulated data is provided.

• Journal of the Korean Data and Information Science Society
• /
• 제22권3호
• /
• pp.459-466
• /
• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 한국수자원학회 2016년도 학술발표회
• /
• pp.92-92
• /
• 2016

최근 도시유역의 홍수들이 빈번히 발생하고 있다. 2010년 2011년에는 서울 지역에서 연이은 도시 홍수피해가 발생하였고, 2015년 미국 South Carolina, 프랑스, 중국 지역에서 많은 홍수 피해가 발생 했다. 도시유역에서 홍수피해가 증가하고 있는 주요 원인은 기후변화로 인한 극한 홍수의 증가, 도시화로 인한 불투수면적의 증가, 배수관망의 통수능 부족 등이라 할 수 있다. 본 연구는 앞서 제시한 세가지 원인들 중 불투수면적의 증가 및 배수관망의 통수능 부족 부분에 대하여 기존 연구와는 다른 시각으로 접근하여 도시유역의 홍수유출 저감방안에 대한 연구를 수행하였다. 본 연구에서는 서울의 신월 배수분구를 대상으로 첫 번째 불투수지역들 중 강우발생시 홍수유출에 직접적인 영향 미치는DCIA(Directly Connected Impervious Area)지역과 IIA(Isolated Impervious Area)를 분리하여 홍수유출 특성을 분석하였고, 두 번째는 배수 관망의 통수능 부족 문제를 해결하기 위해서 관망의 통수능을 증가시키는 방법이 아닌 홍수 유출을 저감할 수 있는 적정 배수관망 레이아웃을 산정한 후 이를 SWMM 모형에 적용하여 기존 배수관망 시스템과 적정 배수관망 시스템의 홍수저감량을 비교하였다. 마지막으로는 첫번째 방법과 두 번째 방법을 동시에 적용하였을 때의 홍수저감량을 검토하여 최적의 도시유역의 홍수유출 저감방법을 제시하고자 한다.

• The Korean Journal of Applied Statistics
• /
• 제18권1호
• /
• pp.173-182
• /
• 2005

A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.

• The Korean Journal of Applied Statistics
• /
• 제15권1호
• /
• pp.119-128
• /
• 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.

• Journal of Applied Reliability
• /
• 제1권2호
• /
• pp.183-192
• /
• 2001

Jelinski-Moranda model and modified Jelinski-Moranda model in software reliability are studied and we consider maximum likelihood estimator and Bayes estimates of the number of faults and the fault-detection rate per fault. A gibbs sampling approach is employed to compute the Bayes estimates, future survival function is examined. Model selection based on prequential likelihood of the conditional predictive ordinates. A numerical example with simulated data set is given.

• Proceedings of the Korea Information Processing Society Conference
• /
• 한국정보처리학회 2001년도 추계학술발표논문집 (상)
• /
• pp.413-416
• /
• 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
• /
• 제15권1호
• /
• pp.73-84
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• 제27권6호
• /
• pp.1069-1076
• /
• 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.

• Journal of Korean Society for Quality Management
• /
• 제27권3호
• /
• pp.125-141
• /
• 1999

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.