• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1125-1128
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• 2008

본 연구에서는 Bayesian MCMC 방법과 2차 근사식을 이용한 최우추정(Maximum Likelihood Estimation, MLE)방법 방법을 이용하여 낙동강 유역의 본류지점인 낙동, 왜관, 고령교, 진동지점에 대한 점 빈도분석을 수행하고 그 결과로써 불확실성을 포함한 빈도곡선을 작성하였다. 통계적 실험을 통한 두 가지 추정방법의 분석을 위하여 먼저 자료의 길이가 100인 8개의 합성 유량자료 셋을 생성하여 비교 연구를 수행하였으며, 이를 자료길이 36인 실측 유량자료의 추정결과와 비교하였다. Bayesian MCMC 방법에 의한 평균값과 2차 근사식을 이용한 취우추정방법에 의한 모드에서의 2모수 Weibull 분포의 모수 추정값은 비슷한 결과를 보였으나, 불확실성을 나타내는 하한값과 상한값의 차이는 Bayesian MCMC 방법이 2차 근사식을 이용한 취우추정방법보다 불확실성을 감소시켜 나타내는 것을 알 수 있었다. 또한 실측 유량자료를 이용한 결과, 2차 근사식을 이용한 최우추정방법의 경우 자료의 길이가 감소됨에 따라 불확실성의 범위가 합성 유량자료를 사용한 경우에 비해 상대적으로 증가되지만, Bayesian MCMC 방법의 경우에는 자료의 길이에 대한 영향이 거의 없다는 결론을 얻을 수 있었다. 그러므로 저수량 빈도분석을 수행하기 위해 충분한 자료를 확보할 수 없는 국내의 상황을 감안할 때, 위와 같은 결론으로부터 Bayesian MCMC 방법이 불확실성을 표현하는데 있어서 2차 근사식을 이용한 최우추정방법에 비해 합리적일 수 있다는 결론을 얻을 수 있었다.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.171-181
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• 2001

The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.

• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.49-63
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• 2008

The Bayesian MCMC(Bayesian Markov Chain Monte Carlo) and the MLE(Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site low flow frequency analysis at the 4 stage stations (Nakdong, Waegwan, Goryeonggyo, and Jindong). Using the results of two types of the estimation method, the frequency curves including uncertainty are plotted. Eight case studies using the synthetic flow data with a sample size of 100, generated from 2-parmeter Weibull distribution are performed to compare with the results of analysis using the MLE and the Bayesian MCMC. The Bayesian MCMC and the MLE are applied to 36 years of gauged data to validate the efficiency of the developed scheme. These examples illustrate the advantages of the Bayesian MCMC and the limitations of the MLE based on a quadratic approximation. From the point of view of uncertainty analysis, the Bayesian MCMC is more effective than the MLE using a quadratic approximation when the sample size is small. In particular, the Bayesian MCMC is a more attractive method than MLE based on a quadratic approximation because the sample size of low flow at the site of interest is mostly not enough to perform the low flow frequency analysis.

• Journal of the Korea Concrete Institute
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• v.22 no.2
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• pp.171-177
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• 2010

Recently, Performance Based Design (PBD) method has been studied as a next generation structural design method, which enables a designed structure to satisfy the required performance during its service life. One method of deciding whether the required performance has been satisfied is Bayesian method, which has been commonly used in seismic analysis. Generally, it is presented as a conditional probability of exceeding some limit state (i.e., collapse) for a given ground motion. In PBD of concrete mixture design, the same methodology can be applied to assess concrete material performance based on some conditional parameters (i.e. strength, workability, carbonation, etc). In this paper, a detailed explanation of the procedure of drawing satisfaction curve by using Bayesian method based on various material parameters is shown. Also, a discussion of using the developed satisfaction curves for PBD for concrete mixture design is presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.316-321
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• 2007

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1121-1124
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• 2008

저수분석(low flow analysis)은 수자원공학에서 중요한 분야 중 하나이며, 특히 저수량 빈도분석(low flow frequency analysis)의 결과는 저수(貯水)용량의 설계, 물 수급계획, 오염원의 배치 및 관개와 생태계의 보존을 위한 수량과 수질의 관리에 중요하게 사용된다. 그러므로 본 연구에서는 저수량 빈도분석을 위한 점빈도분석을 수행하였으며, 특히 빈도분석에 있어서의 불확실성을 탐색하기 위하여 Bayesian 방법을 적용하고 그 결과를 기존에 사용되던 불확실성 탐색방법과 비교하였다. 본 논문의 I편에서는 Bayesian 방법 중 사전분포(prior distribution)와 우도함수(likelihood function)의 복잡성에 상관없이 계산이 가능한 Bayesian MCMC(Bayesian Markov Chain Monte Carlo) 방법과 Metropolis-Hastings 알고리즘을 사용하기 위한 여러과정의 이론적 배경과 Bayesian 방법에서 가장 중요한 요소인 사전분포를 구축하고 이를 비교 및 평가하였다. 고려된 사전분포는 자료에 기반하지 않은 사전분포와 자료에 기반한 사전분포로써 두 사전분포를 이용하여 Metropolis-Hastings 알고리즘을 수행하고 그 결과를 비교하여 저수량 빈도분석에 합리적인 사전분포를 선정하였다. 또한 알고리즘의 수행과정에서 필요한 제안분포(proposal distribution)를 적용하여 그에 따른 알고리즘의 효율성을 채택률(acceptance rate)을 산정하여 검증해 보았다. 사전분포의 분석 결과, 자료에 기반한 사전분포가 자료에 기반하지 않은 사전분포보다 정확성 및 불확실성의 표현에 있어서 우수한 결과를 제시하는 것을 확인할 수 있었고, 채택률을 이용한 알고리즘의 효용성 역시 기존 연구자들이 제시하였던 만족스러운 범위를 가지는 것을 알 수 있었다. 최종적으로 선정된 사전분포는 본 연구의 II편에서 Bayesian MCMC 방법의 사전분포로 이용되었으며, 그 결과를 기존 불확실성의 추정방법의 하나인 2차 근사식을 이용한 최우추정(maximum likelihood estimation)방법의 결과와 비교하였다.

• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.35-47
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• 2008

The low flow analysis is an important part in water resources engineering. Also, the results of low flow frequency analysis can be used for design of reservoir storage, water supply planning and design, waste-load allocation, and maintenance of quantity and quality of water for irrigation and wild life conservation. Especially, for identification of the uncertainty in frequency analysis, the Bayesian approach is applied and compared with conventional methodologies in at-site low flow frequency analysis. In the first manuscript, the theoretical background for the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) method and Metropolis-Hasting algorithm are studied. Two types of the prior distribution, a non-data- based and a data-based prior distributions are developed and compared to perform the Bayesian MCMC method. It can be suggested that the results of a data-based prior distribution is more effective than those of a non-data-based prior distribution. The acceptance rate of the algorithm is computed to assess the effectiveness of the developed algorithm. In the second manuscript, the Bayesian MCMC method using a data-based prior distribution and MLE(Maximum Likelihood Estimation) using a quadratic approximation are performed for the at-site low flow frequency analysis.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.49-56
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• 1997

In emission computed tomography, statistical reconstruction methods in the context of a Bayesian framework have been a topic of interest over the last decade. This was mainly due to the fact that Bayesian approaches can incorporate a priori information into the reconstruction algorithm. Although these approaches can exhibit good performance, their applications to the clinic is hindered mainly by their high computational cost. On the other hand, the speed and simplicity of the filtered backprojection (FBP) algorithm have led to its widespread use in most clinical applications. In this work, we use spline models, which have been quite useful in Bayesian reconstruction, as regularizers for high-frequency apodization in FBP algorithm and show that the effects of using spline models as priors in Bayesian reconstruction can also be achieved in FBP reconstruction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.39-48
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• 2005

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.