• 품질경영학회지
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• 제27권3호
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• pp.125-141
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• 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.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.177-198
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• 2002

The estimation of variance components or variance ratios in linear model is an important issue in plant or animal breeding fields, and various estimation methods have been devised to estimate variance components or variance ratios. However, many traits of economic importance in those fields are observed as dichotomous or polychotomous outcomes. The usual estimation methods might not be appropriate for these cases. Recently threshold linear model is considered as an important tool to analyze discrete traits specially in animal breeding field. In this note, we consider a hierarchical Bayesian method for the threshold animal model. Gibbs sampler for making full Bayesian inferences about random effects as well as fixed effects is described to analyze jointly discrete traits and continuous traits. Numerical example of the model with two discrete ordered categorical traits, calving ease of calves from born by heifer and calving ease of calf from born by cow, and one normally distributed trait, birth weight, is provided.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.851-858
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• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.104-109
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• 2016

In this paper, a localization method for multiple robots based on Bayesian inference is proposed when multiple robots adopting multi-RAT (Radio Access Technology) communications exist in cognitive radio networks. Multiple robots are separately defined by primary and secondary users as in conventional mobile communications system. In addition, the heterogeneous spectrum environment is considered in this paper. To improve the performance of localization for multiple robots, a realistic multiple primary user distribution is explained by using the probabilistic graphical model, and then we introduce the Gibbs sampler strategy based on Bayesian inference. In addition, the secondary user selection minimizing the value of GDOP (Geometric Dilution of Precision) is also proposed in order to overcome the limitations of localization accuracy with Gibbs sampling. Via the simulation results, we can show that the proposed localization method based on GDOP enhances the accuracy of localization for multiple robots. Furthermore, it can also be verified from the simulation results that localization performance is significantly improved with increasing number of observation samples when the GDOP is considered.

• Ocean and Polar Research
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• 제31권2호
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• pp.189-198
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• 2009

Passive air samplers with polyurethane foam (PUF) disks were employed to determine seasonal gas phase variation of polycyclic aromatic hydrocarbons (PAHs) in ambient air on Anmyeon island from March 2007 to January 2008. Sum of 13 PAHs ranged between $3.5\;ng/m^3$ and $27.6\;ng/m^3$. Total PAHs during the heating season was 6.2 times higher than non-heating season. The dominant PAHs components during sampling periods were low and middle molecular weight PAHs including phenanthrene, fluoranthene, pyrene and chrysene. Gas exchange fluxes of PAHs across the air-water interface of the Yellow Sea were calculated using a modified two-film exchange model. PAHs fluxes ranged from $196\;ng/m^2/d$ net volatilization during summer to $3830\;ng/m^2/d$ net absorption during winter. Passive air sampler provides a convenient and cost-effective tool for measuring averaged gas phase PAHs, which was successfully used for calculation of gas exchange flux of PAHs in the Yellow Sea.

• 응용통계연구
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• 제27권6호
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• pp.959-973
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• 2014

자산가격의 비대칭적 변동을 설명하기 위해 최근 비대칭적 점프확산 모형이 제안되었다. 본 논문에서는 이러한 자산가격 모형을 분석하는데 사용되는 효율적인 베이지안 방법을 제안한다. 본 논문에서 제안되는 방법은 모형 요소가 쉽게 추출되는 편의성을 희생하지 않으면서도 조건부 분포들간의 함수적 비호환성을 통해 효율성을 향상시킬 수 있는 부분붕괴 깁스 샘플러를 고안함으로써 개발되었다. 제안된 방법은 모의실험 자료에 적용되어 그 효율성을 검증하였고 1980년 9월부터 2014년 8월까지 관찰된 일별 S&P 500 자료에 적용되었다.

• Nuclear Engineering and Technology
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• 제53권9호
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• pp.2816-2829
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• 2021

Decay heat residuals of spent nuclear fuel (SNF), i.e., the differences between calculations and measurements, were obtained previously for various spent fuel assemblies (SFA) using the Polaris module of the SCALE code system. In this paper, we compare decay heat residuals to their uncertainties, focusing on four PWRs and four BWRs. Uncertainties in nuclear data and model inputs are propagated stochastically through calculations using the SCALE/Sampler super-sequence. Total uncertainties could not explain the residuals of two SFAs measured at GE-Morris. The combined z-scores for all SFAs measured at the Clab facility could explain the resulting deviations. Nuclear-data-related uncertainties contribute more in the high burnup SFAs. Design and operational uncertainties tend to contribute more to the total uncertainties. Assembly burnup is a relevant variable as it correlates significantly with the SNF decay heat. Additionally, burnup uncertainty is a major contributor to decay heat uncertainty, and assumptions relating to these uncertainties are crucial. Propagation of nuclear data and design and operational uncertainties shows that the analyzed assemblies respond similarly with high correlation. The calculated decay heats are highly correlated in the PWRs and BWRs, whereas lower correlations were observed between decay heats of SFAs that differ in their burnups.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.215-223
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• 1996

By using Tukey's generalized lambda distribution, appoximate posterior density is derived under the Bayes-empirical Bayes model. The sensitivity of posterior distribution to the hyperprior distribution is examined by using Tukey's generalized lambda distriburion which approximate many well-knmown distributions. Based upon Monte Varlo simulation studies it can be said that posterior distribution is sensitive to the cariance of the prior distribution and to the symmetry of the hyperprior distribution. Also posterior distribution is approximately obtained by using the following methods : Lindley method, Laplace method and Gibbs sampler method.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.313-318
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• 2000

This article addresses aspects of combining information, with special attention to meta-analysis. In specific, we consider hierarchical Bayesian models for meta-analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. Numerical methods of finding Bayes estimators under these heavy tailed prior are given, and are illustrated with an actual example.

• Journal of applied mathematics & informatics
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• 제9권1호
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• pp.289-301
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• 2002

This paper considers the Bayesian analysis of the regression model wish autoregressive errors. The Bayesian approach for finding the order p of autoregressive error is proposed and the proposed method can be simplified by generalized Savage-Dicky density ratio(Verdinelli and Wasser-man, [18]). And the Markov chain Monte Carlo method(Gibbs sample, [7]) is used in order to overcome the difficulty of Bayesian computations. Final1y, several examples are used to illustrate our proposed methodology.