• Communications for Statistical Applications and Methods
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• 제18권3호
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• pp.295-300
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• 2011

The Bayesian nonparametric estimation of two uniformly stochastically ordered distributions is studied. We propose a restricted Dirichlet Process. Among many types of restriction we consider only uniformly stochastic ordering in this paper since the computation of integrals is relatively easy. An explicit expression of the posterior distribution is given. When square loss function is used the posterior distribution can be obtained by easy integration using some computer program such as Mathematica.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.349-352
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• 2001

We propose one properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$.

• 대기
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• 제19권2호
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• pp.199-211
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• 2009

In this paper, we examine the multiple change-point and change pattern in the 54 years (1954-2007) time series of the annual and the heavy precipitation characteristics (amount, days and intensity) averaged over South Korea. A Bayesian approach is used for detecting of mean and/or variance changes in a sequence of independent univariate normal observations. Using non-informative priors for the parameters, the Bayesian model selection is performed by the posterior probability through the intrinsic Bayes factor of Berger and Pericchi (1996). To investigate the significance of the changes in the precipitation characteristics between before and after the change-point, the posterior probability and 90% highest posterior density credible intervals are examined. The results showed that no significant changes have occurred in the annual precipitation characteristics (amount, days and intensity) and the heavy precipitation intensity. On the other hand, a statistically significant single change has occurred around 1996 or 1997 in the heavy precipitation days and amount. The heavy precipitation amount and days have increased after the change-point but no changes in the variances.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.825-834
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• 2002

Poisson processes are widely used in reliability and survival analysis. In particular, multiple event time data in survival analysis are routinely analyzed by use of Poisson processes. In this paper, we consider large sample properties of nonparametric Bayesian models for Poisson processes. We prove that the posterior distribution of the cumulative intensity function of Poisson processes is consistent under regularity conditions on priors which are Levy processes.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• 응용통계연구
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• 제7권2호
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• pp.9-19
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• 1994

평가자간 평가 일치도(measure of agreement)를 나타내는 모수 $\kappa$와 양성 반응 비율 $\mu$를 지닌 베타-이항 분포 모형은 심리학 분야에서 많이 다루어지는 모형이다. 이 모형에서 $\kappa$에 대한 추정은 $\mu$가 0에 가까운 값을 가질 때 우도함수를 이용한 전통적 추론 방법의 적용이 어렵다. 본 논문에서는 이러한 문제를 Gibbs sampler를 이용한 Bayesian 분석 방법을 적용시켜 주변 사후 밀도 함수를 추정하였으며 이를 이용하여 Bayesian 추정값도 구하였다.

• 한국안전학회지
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• 제36권2호
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• pp.32-38
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• 2021

This paper suggests an approach to evaluate the reliability of an intelligent power module with information deficiency of prior distribution and the characteristics of censored data through Bayesian statistics. This approach used a prior distribution of Bayesian statistics using the lifetime information provided by the manufacturer and compared and evaluated diffuse prior (vague prior) distributions. To overcome the computational complexity of Bayesian posterior distribution, it was computed with Gibbs sampling in the Monte Carlo simulation method. As a result, the standard deviation of the prior distribution developed using simple information was smaller than that of the posterior distribution calculated with the diffuse prior. In addition, it showed excellent error characteristics on RMSE compared with the Kaplan-Meier method.

• 응용통계연구
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• 제15권1호
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• pp.73-84
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• 2002

계수에 대한 부등 제한조건이 있는 선형 회귀모형은 경제모형에서 가장 흔하게 다루어지는 것 중의 하나이다. 이는 특정 설명변수에 대한 계수의 부호를 음양 중 하나로 제한하거나 계수들에 대하여 순서적 관계를 주기 때문이다. 본 논문에서는 이러한 부등 제한이 있는 선형회귀 모형에서 유의한 설명변수의 선택을 해결하는 베이지안 기법을 고려한다. 베이지안 변수선택은 가능한 모든 모형의 사후확률 계산이 요구되는데 본 논문에서는 이러한 사후확률들을 동시에 계산하는 방법을 제시한다. 구체적으로 가장 일반적인 모형의 모수에 대한 사후표본을 깁스 표본기법을 적용시켜 얻은 후 이를 이용하여 모든 가능한 모형의 사후확률을 계산하고 실제적인 자료에 본 논문에서 제안된 방법을 적용시켜 본다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.205-208
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• 2005

We have fuzzy hypotheses testing from Bayesian statistics with ideas from fuzzy sets theory to generalize Bayesian methods both for samples of fuzzy data and for prior distributions with non-precise parameters. Appling the principle of agreement index, the posterior odds ratio in the favor of hypotheses $H_0$ is equal to product of the fuzzy odds ratio and the fuzzy likelihood ratio. If the Posterior odds ratio exceeds the grade judgement, we accept the hypothesis $H_0$ for the degree.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.569-574
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• 2010

We consider two components system which have Freund's bivariate exponential model. In this case, Bayesian multiple comparisons procedure for failure rates is sug-gested in K Freund's bivariate exponential populations. Here we assume that the com-ponents enter the study at random over time and the analysis is carried out at some prespeci ed time. We derive fractional Bayes factor for all comparisons under non- informative priors for the parameters and calculate the posterior probabilities for all hypotheses. And we select a hypotheses which has the highest posterior probability as best model. Finally, we give a numerical examples to illustrate our procedure.