• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.753-763
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• 1997

컴퓨터의 발전에 따른 MCMC를 비동질적 포아송 과정에 이용하였다. 베이지안 추론에서 조건부 분포를 가지고 사후분포를 결정하는데 있어서의 계산 문제를 고려하였다. 특히 분포가 이중지수, 곰페르츠, 랄리, 감마, 그리고 검벨인 일반 순서통계량 모형에 대하여 깁스 샘플링과 메트로폴리스 알고리즘을 활용한 베이지안 계산과 모형선택을 제시하였다.

• Journal of Korean Institute of Industrial Engineers
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• v.6 no.2
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• pp.37-47
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• 1980

A system of conditional sampling plans composed of three stages is developed. In the first stage, the decision to accept or reject the lot is based on the information obtained from the current lot. When a decision is not made in the first stage, a second stage is introduced and the information from the immediately preceding lot as well as the information from the current lot is used for the decision. When a decision is not made in the first stage, a second stage is introduced and the information from the immediately preceding lot as well as the information from the current lot is used for the decision. When a decision is not made in the second stage either, the decision is deferred until the information from the immediately following lot is obtained. Existing tables for constructing double sampling plans with $_2$=$2n_1$ can be used to find the parameters of these plans. These sampling plans can bring sizable savings in the amount of inspection when the process is relatively stable. The response delay to the change in process quality and the deferred events may be considered as shortcomings of these plans. However, these are not serious in practical applications, and the reduction in sample size may more than offset these shortcomings.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.336-338
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• 2012

본 연구에서는 고차 데이터 분류를 위해 순차적 베이지만 샘플링 기반의 진화연산 기법을 이용한 하이퍼네트워크 모델의 학습 알고리즘을 제시한다. 제시하는 방법에서는 모델의 조건부 확률의 사후(posterior) 분포를 최대화하도록 학습이 진행된다. 이를 위해 사전(prior) 분포를 문제와 관련된 사전지식(prior knowledge) 및 모델 복잡도(model complexity)로 정의하고, 측정된 모델의 분류성능을 우도(likelihood)로 사 용하며, 측정된 사전분포와 우도를 이용하여 모델의 적합도(fitness)를 정의한다. 이를 통해 하이퍼네트워크 모델은 고차원 데이터를 효율적으로 학습 가능할 뿐이 아니라 모델의 학습시간 및 분류성능이 개선될 수 있다. 또한 학습 시에 파라미터로 주어지던 하이퍼에지의 구성 및 모델의 크기가 학습과정 중에 적응적으로 결정될 수 있다. 제안하는 학습방법의 검증을 위해 본 논문에서는 약 25,000개의 유전자 발현정보 데이터셋에 대한 분류문제에 모델을 적용한다. 실험 결과를 통해 제시하는 방법이 기존 하이퍼네트워크 학습 방법 뿐 아니라 다른 모델들에 비해 우수한 분류 성능을 보여주는 것을 확인할 수 있다. 또한 다양한 실험을 통해 사전분포로 사용된 사전지식이 모델 학습에 끼치는 영향을 분석한다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.505-514
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• 2000

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.263-275
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• 2014

The present study used a hierarchical Bayesian approach was used to develop a mixed effect model to describe the transitional behavior of subjects in time nonhomogeneous Markov chains. The posterior distributions of model parameters were not in analytically tractable forms; subsequently, a Gibbs sampling method was used to draw samples from full conditional posterior distributions. The proposed model was implemented with real data.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.319-322
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• 2006

The flow characteristics around a floating cylinder in a swirling flow field in a vertical pipe with a length of 600mm and an inner diameter of 100mm is investigated by the use of the Stereoscopic-PIV system. The measurement system consists of two cameras, a Nd-Yag laser and a host computer. Optical sensors(LEDs) were used to detect the location of the floating cylinder and to activate the Stereoscopic-PIV system. A conditional sampling Stereoscopic-PIV system was developed in which the flow fields around the floating cylinder are measured at the events of the activations. It has been verified that the motion of the floating cylinder becomes stable when the azimuthal velocity component of the swirl flow is maintained at stable states.

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.1
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• pp.60-72
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• 1994

The effect of free stream tubulence on the coherent structure in the near wake of a circular cylinder was investigated by a conditional sampling technique. The measurements were made from C.T.A. with hot wire I-probe and a Split-film sensor. Contours of phase-averaged velocity and vorticities were presented and discussed. It was found that the value of the vortex strength increased with increasing free stream turbulence which can enhance the roll-up of the shear layer.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.311-325
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• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.