• 대한전자공학회논문지SP
• /
• 제42권6호
• /
• pp.43-54
• /
• 2005

이본 논문에서는 Bayesian 영상 분할법과 SOM(Self Organization feature Map)을 이용한 텍스쳐(Texture) 분할 방법을 제안한다. SOM의 입력으로 다중 스케일에서의 웨이블릿 계수를 사용하고, 훈련된 SOM으로부터 관측 데이터에 대한 우도(尤度, likelihood)와 사후확률을 구하는 방법을 제시한다. 훈련된 SOM들로부터 구한 사후확률과 MAP(Maximum A Posterior) 분류법을 이용하여 텍스쳐 분할을 얻는다. 그리고 문맥 정보를 이용하여 텍스쳐 분할 결과를 개선하였다. 제안 방법은 HMT(Hidden Markov Tree)을 이용한 텍스쳐 분할보다 더 우수한 결과를 보여준다. 또한 SOM과 HMTseg라고 불리는 다중스케일 Bayesian 영상 분할 기법을 이용한 텍스쳐 분할 결과는 HMT와 HMTseg을 이용한 결과보다 더 우수한 성능을 보여준다.

• 산업경영시스템학회지
• /
• 제28권3호
• /
• pp.26-35
• /
• 2005

This paper is intended to compare the hazard rate estimations from Bayesian approach and maximum likelihood estimate(MLE) method. Hazard rate frequently involves unknown parameters and it is common that those parameters are estimated from observed data by using MLE method. Such estimated parameters are appropriate as long as there are sufficient data. Due to various reasons, however, we frequently cannot obtain sufficient data so that the result of MLE method may be unreliable. In order to resolve such a problem we need to rely on the judgement about the unknown parameters. We do this by adopting the Bayesian approach. The first one is to use a predictive distribution and the second one is a method called Bayesian estimate. In addition, in the Bayesian approach, the prior distribution has a critical effect on the result of analysis, so we introduce the method using computerized-simulation to elicit an effective prior distribution. For the simplicity, we use exponential and gamma distributions as a likelihood distribution and its natural conjugate prior distribution, respectively. Finally, numerical examples are given to illustrate the potential benefits of the Bayesian approach.

• 한국수자원학회논문집
• /
• 제41권3호
• /
• pp.325-340
• /
• 2008

본 연구는 저수량 지역 빈도분석(regional low flow frequency analysis)을 수행하기 위하여 일반최소자승법(ordinary least squares method)을 이용한 Bayesian 다중회귀분석을 적용하였으며, 불확실성측면에서의 효과를 탐색하기 위하여 Bayesian 다중회귀분석에 의한 추정치와 t 분포를 이용하여 산정한 일반 다중회귀분석의 추정치의 신뢰구간을 비교분석하였다. 각 재현기간별 비교결과를 보면 t 분포를 이용하여 산정된 평균 추정치와 Bayesian 다중회귀분석에 의한 평균 추정치는 크게 다르지 않았다. 그러나 불확실성 측면에서 평가해볼 때 신뢰구간의 상한추정치와 하한추정치의 차이는 Bayesian 다중회귀분석을 사용한 경우가 기존 방법을 사용한 경우보다 훨씬 작은 것으로 나타났으며, 이로부터 저수량(low flow) 지역 빈도분석을 수행하는 경우 Bayesian 다중회귀분석이 일반 회귀분석보다 불확실성을 표현하는데 있어서 우수하다는 결과를 얻을 수 있었다. 또한 낙동강 유역에 2개의 미계측 유역을 선정하고 구축된 Bayesian 다중회귀모형을 적용하여 불확실성을 포함한 미계측 유역에서의 저수량(low flow)을 추정하였으며 이와 같은 방법이 미계측 유역에서의 저수(low flow) 특성을 나타내는 데 있어서 효과적일 수 있음을 입증하였다.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제33권10호
• /
• pp.877-885
• /
• 2006

최근에 대화형 에이전트에서 사용자 질의의 주제 추론을 위하여 베이지안 네트워크가 효과임이 발표되었다. 하지만 베이지안 네트워크는 설계에 있어서 많은 시간이 소요되며, 스크립트(대화를 위한 데이타베이스)의 추가 변경시에는 베이지안 네트워크도 같이 수정해야 하는 번거로움이 있어 대화형 에이전트의 확장성을 저해하고 있다. 본 논문에서는 스크림트로부터 베이지안 네트워크를 자동으로 생성함으로써 베이지안 네트워크를 이용한 대화형 에이전트의 확장성을 높이는 방법을 제안한다. 제안한 방법은 베이지안 네트워크의 구성노드를 계층적으로 설계하고, Noisy-OR gate를 사용하여 베이지안 네트워크의 조건부 확률 테이블을 구성한다. 피험자 10명이 대화형 에이전트를 위한 베이지안 네트워크를 수동 설계한 것과 비교한 결과 제안하는 방법이 효과적임을 알 수 있었다.

• 한국조사연구학회:학술대회논문집
• /
• 한국조사연구학회 2006년도 춘계학술대회 발표논문집
• /
• pp.131-159
• /
• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.

• Journal of the Korean Statistical Society
• /
• 제20권1호
• /
• pp.44-56
• /
• 1991

For all pairwise comparisons of treatments, Bayesian simultaneous confidence intervals are proposed and studied. First Bayesian solutions are obtained for a fixed prior, and then prior parameters are estimated by a parametric empirical Bayesian method. The nominal confidence level is shown to be controlled asymptotically. An extension to the unbalanced design is also considered.

• Journal of the Korean Data and Information Science Society
• /
• 제17권1호
• /
• pp.233-244
• /
• 2006

In this paper, we develop the Bayesian multiple comparisons procedure for the binomial distribution. We suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. An example is illustrated for the proposed method. For this example, the suggested method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison. Also, some simulation was performed.

• Journal of the Korean Statistical Society
• /
• 제34권3호
• /
• pp.245-261
• /
• 2005

The marginal distribution of X is considered when (X, Y) has a truncated bivariate t-distribution. This paper mainly focuses on the marginal nontruncated distribution of X where Y is truncated below at its mean and its observations are not available. Several properties and applications of this distribution, including relationship with Azzalini's skew-normal distribution, are obtained. To circumvent inferential problem arises from adopting the frequentist's approach, a Bayesian method utilizing a data augmentation method is suggested. Illustrative examples demonstrate the performance of the method.

• 한국신뢰성학회지:신뢰성응용연구
• /
• 제17권3호
• /
• pp.213-223
• /
• 2017

Purpose: The purpose of this study is to arrange the life data analysis literatures based on the Bayesian method quantitatively and provide it as tables. Methods: The Bayesian method produces a more accurate estimates of other traditional methods in a small sample size, and it requires specific algorithm and prior information. Based on these three characteristics of the Bayesian method, the criteria for classifying the literature were taken into account. Results: In many studies, there are comparisons of estimation methods for the Bayesian method and maximum likelihood estimation (MLE), and sample size was greater than 10 and not more than 25. In probability distributions, a variety of distributions were found in addition to the distributions of Weibull commonly used in life data analysis, and MCMC and Lindley's Approximation were used evenly. Finally, Gamma, Uniform, Jeffrey and extension of Jeffrey distributions were evenly used as prior information. Conclusion: To verify the characteristics of the Bayesian method which are more superior to other methods in a smaller sample size, studies in less than 10 samples should be carried out. Also, comparative study is required by various distributions, thereby providing guidelines necessary.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권3호
• /
• pp.135-142
• /
• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.