• 품질경영학회지
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• 제22권1호
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• pp.96-112
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• 1994

This article studies the design of Bayesian single attribute acceptance sampling plans under dependent production processes. An economic model is constructed by extending the mathematical model developed for non-Bayesian cases for Bayesian cases. The mathematical structure of the model is analyzed and it is used to prove that optimization of the model can be achieved by applying the solution method developed for non-Bayesian models directly. The effect of dependence patterns and the types of prior distributions on the design of sampling plans is also investigated through a computational study.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.572-578
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• 2008

본 논문에서는 대규모 베이지안 망 구조 학습을 위해 제안되었던 R-CORE 방법의 탐색 공간의 크기에 대한 개략적인 분석과 실제 문제에 적용하였을 경우의 효과에 대한 실험적 결과를 제시한다. R-CORE 방법은 베이지안 망 구조 학습의 탐색 공간을 축소하기 위해 제안된 확률변수들의 재귀적 군집화와 오더 제한 방법이다. 알려진 벤치마크 베이지안 망을 이용한 분석을 통해, 제안되었던 R-CORE 방법이 worst case에는 기존의 방법과 유사한 탐색 공간을 가지나 평균적으로 기존방법보다 훨씬 적은 탐색 공간만을 고려한다는 것을 보인다. 또한 평균적으로 훨씬 적은 탐색 공간만을 고려하는 결과, 구조 탐색에서 기존 방법에 비해 상대적으로 적은 overfitting이 일어남을 실험적으로 보인다.

• 약학회지
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• 제41권2호
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• pp.195-202
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• 1997

The purpose of this study was to determine pharmacokinetic parameters of vancomycin using two point calculation(TPC) and Bayesian methods in 16 Korean normal volunteers and 15 g astric cancer patients. Nonparametric expected maximum(NPEM) algorithm for calculation of population pharmacokinetic parameter was used, and these parameters were applied for clinical pharmacokinetic parameters by Bayesian analysis. Vancomycin was administered 1.0g every 12 hrs for 3 days by IV infusion over 60 minutes. The volume of distribution(Vd), elimination rate constant(Kel) and total body clearance(CLt) of vancomycin in normal volunteers using TPC method were $0.34{\pm}0.06 L/kg,\; 0.19{\pm}0.01 hr^{-1}$ and $4.08 {\pm} 0.93 L/hr$, respectively, The Vd, Kel and CLt of vancomycin in gastric cancer patients using TPC method were $0.46 {\pm} 0.06 L/kg, 0.17{\pm}0.02 hr^{-1}$ and $4.84 {\pm} 0.57 L/hr$ respectively. There were significant differences(p<0.05) in Vd. Kel and CLt between normal volunteers and gastric cancer patients. Polpulation pharmacokinetic parameter, the slope(KS) of the relationship beetween Kel versus creatinine Clearance, and the Vd were $0.00157{\pm}0.00029(hr{\cdot}mL/min/1.73m^2)^{-1},\; 0.631 {\pm} 0.0036 L/kg$ in gastric cancer patients using NPEM algorithm respectively. The Vd and Kel were $0.63{\pm}0.005 L/kg, 0.15 {\pm}0.027 hr^{-1}$ for gastric cancer patients using Bayesian method. There were significant differences(p<0.05) in vancomycin pharmacokinetics between Bayesian and TPC methods. It is considered that the population parameter in the patient population is necessary for effective Bayesian method in clinical pharmacy practise.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• Asian-Australasian Journal of Animal Sciences
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• 제18권8호
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Geomechanics and Engineering
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• 제27권6호
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• pp.605-614
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• 2021

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

• 응용통계연구
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• 제33권3호
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• pp.309-320
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• 2020

방향성 비순환 그래프(directed acyclic graph; DAG)라고도 하는 베이지안 네트워크(Bayesian network)는 변수 사이의 관계를 확률과 그래프를 통해 모형화할 수 있다는 점에서 최근 의학, 기상학, 유전학 등 여러 분야에서 다양하게 활용되고 있다. 특히 이산형 자료의 예측에 사용되는 베이지안 네트워크 분류분석기(Bayesian network classifier)가 최근 새로운 데이터 마이닝 기법으로 주목받고 있다. 베이지안 네트워크는 그 구조와 학습 방법에 따라 여러 가지 다양한 모형으로 분류할 수 있다. 본 논문에서는 서로 다른 성질을 가진 이산형 자료를 바탕으로 구조 학습 방법에 차이를 두어 베이지안 네트워크 모형을 학습시킨 후, 가장 간단한 방법인 나이브 베이즈 (naïve Bayes) 모형과 비교해 본다. 학습된 모형들을 여러 가지 실제 데이터에 적용하여 그 예측 정확도를 비교함으로써 최적의 분류 분석 결과를 얻을 수 있는지 살펴본다. 또한 각각의 모형에서 나타나는 그래프를 통해 데이터의 변수 사이의 관계를 비교한다.

• 한국측량학회지
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• 제6권1호
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• pp.35-41
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• 1988

지상사진에 의한 삼차원변형해석을 하는데 있어서 변위양계산의 정확도를 향상시키기 위해 반복경증률 상사변환법이 사용되었으며, 변위점검출에서는 Bayesian Inference가 적용되었고, 변위형태해석을 위해 변위방정식을 이용하는 방법을 제시하였다. 그 결과 변위양계산에서는 최소절대법($\Sigma$$\mid$d$\mid$⇒min)에 의한 경중률조건이 정확도를 향상시켰으며, 또한 Bayesian Inference을 적용하므로써 정확한 변위점검출을 할 수 있었다. 변위형태해석에서는 최적변위방정식을 이용하여 대상들의 전체 또는 부분적인 움직임을 해석할 수 있었다.

• Structural Engineering and Mechanics
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• 제69권4호
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• pp.361-369
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• 2019

The determination of Paris' law parameters based on crack growth experiments is an important procedure of fatigue life assessment. However, it is a challenging task because it involves various sources of uncertainty. This paper proposes a novel probabilistic method, termed the S-N Paris law (SNPL) method, to quantify the uncertainties underlying the Paris' law parameters, by finding the best estimates of their statistical parameters from the S-N curve data using a Bayesian approach. Through a series of steps, the SNPL method determines the statistical parameters (e.g., mean and standard deviation) of the Paris' law parameters that will maximize the likelihood of observing the given S-N data. Because the SNPL method is based on a Bayesian approach, the prior statistical parameters can be updated when additional S-N test data are available. Thus, information on the Paris' law parameters can be obtained with greater reliability. The proposed method is tested by applying it to S-N curves of 40H steel and 20G steel, and the corresponding analysis results are in good agreement with the experimental observations.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1996년도 봄 학술발표회 논문집
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• pp.155-166
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• 1996