• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.517-531
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• 2013

보증 데이터를 통해 제품의 수명 및 형상모수를 추정할 때 최우추정법과 같은 전통적인 통계 분석방법(Classical Statistical Method)을 많이 사용하였다. 그러나 전통적인 통계 분석방법을 통해 수명과 형상모수의 추정 시 표본의 크기가 작거나 불완전한 경우 추정량의 신뢰성이 떨어진다는 단점이 있고 또 누적된 경험과 과거자료를 충분히 이용하지 못하는 단점도 있다. 이러한 문제점을 해결하기 위해 모수의 사전분포를 가정하는 베이지안(Bayesian) 기법의 적용이 필요하다. 하지만 보증 데이터분석에 있어서 베이지안 기법을 이용한 연구는 아직 미흡한 실정이다. 본 연구에서는 수명분포가 와이블 분포를 갖는 보증데이터를 활용하여 모수 추정의 효율성을 비교 분석하고자 한다. 이를 위해 와이블 분포의 모수가 대수정규분포를 따르는 사전분포를 갖는 베이지안 기법과 전통적 통계기법인 생명표법(Actuarial method)을 활용하여 추정량을 도출하고 비교 분석하였다. 이를 통해 충분한 관측 데이터를 확보할 수 없는 경우에 베이지안 기법을 이용한 보증 데이터 분석방법의 성능을 확인하고자 한다.

• Journal of the KSME
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• v.54 no.2
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• pp.46-51
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• 2014

최근 각종 공학문제의 해결을 위해 결정론적(Deterministic) 관점보다는 각종 불확실성을 고려한 확률적 (Probabilistic) 또는 추계적(Stochastic) 관점에서 분석 평가하려는 노력이 증가하고 있다. 이는 신뢰성분석 및 설계 측면에서 특히 중요하게 인식되고 있으며, 이를 구현하는 방법론으로 베이지안 통계기법(Bayesian Statistics)이 최근 많은 주목을 받고 있다. 이 글에서는 이러한 기법을 신뢰성 평가 및 고장예지에 어떻게 적용하고 있는지 사례를 중심으로 설명하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.1-13
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• 2021

The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of the Bayesian method from the birth to the present, and the achievements of Bayesian statisticians are summarized. Next, the derivation of the Bayes' theorem, which is the basis of the Bayesian method, is presented, and the roles of the three elements of the Bayes' theorem: priori distribution, likelihood function, and posteriori distribution are explained. In addition, the unique features and advantages of the Bayesian statistics are summarized. Finally, the cases in water resources where the Bayesian method is applied are summarized by dividing them into several categories. With a prevalence of information and big data in the future, the Bayesian method is expected to be used more actively in the water resources field.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.323-333
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• 2011

We analyzed using logistic to find factors with a mental disorder because logistic is the most efficient way assess risk factors. In this paper, we applied data mining techniques that are logistic, neural network, c5.0, cart and Bayesian network to delirium data. The Bayesian network method was chosen as the best model. When delirium data were applied to the Bayesian network, we determined the risk factors associated with delirium as well as identified the network between the risk factors.

• Computational Structural Engineering
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• v.28 no.3
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• pp.7-12
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• 2015

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.77-86
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• 2004

In this study, we applied the Bayesian Network for the case of the mode choice models using the Seoul metropolitan area's house trip survey Data. Sex and age were used lot the independent variables for the explanation or the mode choice, and the relationships between the mode choice and the travellers' social characteristics were identified by the Bayesian Network. Furthermore, trip and mode's characteristics such as time and fare were also used for independent variables and the mode choice models were developed. It was found that the Bayesian Network were useful tool to overcome the problems which were in the traditional mode choice models. In particular, the various transport policies could be evaluated in the very short time by the established relation-ships. It is expected that the Bayesian Network will be utilized as the important tools for the transport analysis.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2016.10a
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• pp.61-61
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• 2016

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.151-161
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• 1997

An easy Monte Carlo Gibbs sampling approach is suggested for Bayesian analysis of cumulative logit models for ordinal polytomous data. Because in the cumulative logit model the posterior conditional distributions of parameters are not given in convenient forms for random sample generation, appropriate latent variables are introduced into the model so that in the new model all the conditional distributions are given in very convenient forms for implementation of the Gibbs sampler.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.