• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.139-151
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• 2002

This study is concerned with model selection and diagnostics for nonlinear regression model through Bayes factor. In this paper, we use informative prior and simulate observations from the posterior distribution via Markov chain Monte Carlo. We propose the Laplace approximation method and apply the Laplace-Metropolis estimator to solve the computational difficulty of Bayes factor.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.90-92
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• 2000

데이터마이닝 문제는 데이터를 그 속성들에 따라 분류하여 예측하는 것뿐만 아니라 분류된 속성들간의 연관성에 대해 잘 설명할 수 있어야 한다. 일반적으로 변수들간의 연관성을 잘 설명할 수 있으면서도 높은 예측력을 가지는 방법으로는 베이지안 네트웍 분류자(Bayesian network classifier)가 있다. 그러나 이것은 데이터 마이닝과 같은 대용량 데이터에서는 성능이 떨어지는 단점이 있다. 이에 이 논문에서는 최근 RBF 신경망이 입력변수 선정문제에 성공적으로 적용된 Reversible Jump Markov Chain Monte Carlo 방법을 이용하여 최적의 입력변수들만을 선택하여 베이지안 네트웍을 학습하는 Selective BN Augmented Naive-Bayes Classifier를 새로운 방안으로 제안하고 이를 실제 데이터마이닝 문제에 적용한 결과를 제시한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.173-173
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• 2018

본 연구는 기상-수문 분야에서 고해상도 수문기상요소를 산출하기 위해 WRF-Hydro(Weather Research and Forecasting and Model Hydrological modeling extension package) 모형을 한반도 대상으로 구축하였다. 모형은 미국 대기 연구 국립센터(NOAA)에서 개발된 커뮤니티형 고해상도 예측모델이므로 미국 등에서 활발히 활용되기 시작하였으나 아직 우리나라 적용성에 대한 연구는 많지 않다. 본 연구에서는 WRF-Hydro 모형을 한반도에 적절히 사용하기 위해 표면유출, 보수깊이, 표면거칠기와 같은 파라미터를 보정하였다. WRF-Hydro는 지역 기상모형인 WRF와 연계하여 coupled WRF/WRF-Hydro 모형을 구동하였으며, 고해상도 유출값을 얻기 위해 미국 지질조사국(USGS)에서 제공한 HydroSHEDS(Hydrological data and map based on SHuttle Elevation Derivatives at multiple Scales)를 이용하였다. 본 연구에서는 관측된 유출값을 Markov Chain Monte Carlo(MCMC) 방법을 활용하여 모형값과 비교하여 파라미터 보정을 수행하였으며, 파라미터 보정된 WRF/WRF-Hydro를 활용해 한반도 과거 홍수 및 가뭄 사상을 모의하여 결과를 분석하였다.

• Asian Journal of Innovation and Policy
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• v.7 no.3
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• pp.625-645
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• 2018

This paper's objective is to draw a decision guideline to continue research and development (R&D) investments in biotech start-ups facing the "Valley of Death" syndrome - a long negative profit period during a financial crisis. The data include financial indices as Net income, Revenues, Total stockholders' equity, Cash & equivalents, and R&D expenses of 18 major biotech companies (nine in negative profit and nine positive, in FY2008) and 15 major pharmaceutical corporations as benchmarks both in FY2008 and in FY2016 derived from the US SEC Database, EDGAR. A first methodology dealing with real options analysis assumes Total stockholders' equity as a growth option. And a second methodology, Bayesian Markov chain Monte Carlo (MCMC) analysis, is applied to test the probability relationship between the Total stockholders' equity and the R&D expenses in these three groups. This study confirms that Total stockholders' equity can play the role of a call option to support continuing R&D investments even in negative profits.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.41.1-41.1
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• 2019

We consider dynamical dark energy models based on a minimally coupled scalar field with three different potentials: the inverse power-law, SUGRA and double exponential potentials. For each model, we derived perturbation initial conditions in the early epoch and performed the Markov Chain Monte Carlo (MCMC) analysis to explore the parameter space that is favored by the current cosmological observations like Planck CMB anisotropy, type Ia supernovae, and baryon acoustic oscillation data. The analysis has been done by using the modified CAMB/COSMOMC code in which the dynamical evolution of the scalar field perturbations are fully considered. The MCMC constraints on the cosmological as well as potential parameters are derived. In the talk we will present a progress report.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2747-2757
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• 2018

We consider the MLE (maximum likelihood estimate) and Bayesian estimates of three-parameter bathtub-shaped lifetime distribution based on the progressive type II censoring with binomial removal. Jung, Chung (2018) proposed the three-parameter bathtub-shaped distribution which is the extension of the two-parameter bathtub-shaped distribution given by Zhang (2004). Jung, Chung (2018) investigated its properties and estimations. The maximum likelihood estimates are computed using Newton-Raphson algorithm. Also, Bayesian estimates are obtained under the balanced loss function using MCMC (Markov chain Monte Carlo) method. In particular, BSEL (balanced squared error loss) function is considered as a special form of balanced loss function given by Zellner (1994). For comparing theirs MLEs with the corresponding Bayes estimates, some simulations are performed. It shows that Bayes estimates is better than MLEs in terms of risks. Finally, concluding remarks are mentioned.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.239-250
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• 2022

In many applications, we frequently encounter correlated multiple outcomes measured on the same subject. Joint modeling of such multiple outcomes can improve efficiency of inference compared to independent modeling. For instance, in developmental toxicity studies, fetal weight and number of malformed pups are measured on the pregnant dams exposed to different levels of a toxic substance, in which the association between such outcomes should be taken into account in the model. The number of malformations may possibly have many zeros, which should be analyzed via zero-inflated count models. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint modeling framework for continuous and count outcomes with excess zeros. In our model, zero-inflated Poisson (ZIP) regression model would be used to describe count data, and a subject-specific random effects would account for the correlation across the two outcomes. We implement a Bayesian approach using MCMC procedure with data augmentation method and adaptive rejection sampling. We apply our proposed model to dose-response analysis in a developmental toxicity study to estimate the benchmark dose in a risk assessment.

• Journal of Korea Water Resources Association
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• v.50 no.4
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• pp.277-288
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• 2017

A probabilistic risk analysis of levee system estimates the overall level of flood risk associated with the levee system, according to a series of possible flood scenarios. It requires the uncertainty analysis of all the risk components, including hydrological, hydraulic and geotechnical parts computed by employing MCMC (Markov Chain Monte Carlo), MCS (Monte Carlo Simulation) and FOSM (First-Order Second Moment), presents a joint probability combined each probability. The methodology was applied to a 12.5 km reach from upstream to downstream of the Gangjeong-Goryeong weir, including 6 levee reaches, in Nakdong river. Overtopping risks were estimated by computing flood stage corresponding to 100/200 year high quantile (97.5%) design flood causing levee overflow. Geotechnical risks were evaluated by considering seepage, slope stability, and rapid drawdown along the levee reach without overflow. A probability-based compound risk will contribute to rising effect of safety and economic aspects for levee design, then expect to use the index for riverside structure design in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.394-404
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• 2013

In the present study, the characteristics of spectral peakedness parameter $Q_p$, bandwidth parameter ${\varepsilon}$, and spectral width parameter ${\nu}$ were analyzed as a first step to define the swell waves quantitatively. For the analysis, the joint probability density function of significant wave heights and peak periods were newly developed. The MCMC(Markov Chain Monte Carlo) simulations have been performed to generate the significant wave heights and peak periods from the developed probability density functions. Applying the simulated significant wave heights and peak periods to the theoretical wave spectrum models, the spectral shapes parameters were obtained and analyzed. Among the spectral shape parameters, only the spectral peakedness parameter $Q_p$, is shown to be independent with the significant wave height and peak wave period. It also best represents the peakedness of the spectral shape, and henceforth $Q_p$ should be used to define the swell waves with a wave period. For the field verification of the results, wave data obtained from Hupo port and Ulleungdo were analyzed and results showed the same trend with the MCMC simulation results.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.45-45
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• 2017

상이한 자연현상으로 발생된 자료들은 때때로 통계적으로 다른 특성을 가지는 경우가 있다. 이런 자료들은 다른 두 개 이상의 모집단에서 자료가 발생한 것으로 가정할 수 가 있다. 기존에 널리 사용되어온 분포형 모형의 경우 단일한 모집단으로부터 자료가 발생한다는 가정하에서 개발된 모형들로 위에서 언급한 자료들을 적절히 모의할 수 없다. 이런 상이한 모집단에서 발생된 자료를 모형화 하기 위해서 혼합분포모형(mixture distribution)이 개발되었다. 홍수나 가뭄 등과 같은 극치 사상의 경우 다양한 자연현상들로부터 발생하기에 혼합분포모형을 적용할 경우 보다 정확한 모의가 가능하다. 혼합분포모형은 두 개 이상의 비혼합분포모형들을 가중합하여 만들어진다. 혼합 분포모형의 형태로 인하여 기존의 분포형 모형의 매개변수 추정 모형으로 널리 사용되던 최우도법 (maximum likelihood method), 모멘트법(method of moment), 확률가중모멘트법 (probability weighted moment method) 등을 이용하여 혼합분포모형의 매개변수를 추정하는 것이 용이 하지 않다. 혼합분포모형의 매개변수 추정 방법으로는 Expectation-Maximization (EM) 알고리즘, Meta-Heuristic Maximum Likelihood (MHML) 방법, Markov Chain Monte Carlo (MCMC) 방법 등이 적용되고 있다. 현재까지 수자원 분야에서 사용되는 극치 자료를 혼합분포모형을 이용하여 모의할 때 매개변수 추정방법에 따른 특성에 대한 연구가 진행되지 않았다. 본 연구에서는 우리나라 연최대강우량 자료를 이용하여 혼합분포모형의 매개변수 추정방법 (EM 알고리즘, MHML 방법, MCMC 방법) 들의 특성들을 비교 분석하였다. 혼합분포모형으로는 Gumbel-Gumbel 혼합분포 모형을 적용하였다. 본 연구의 결과는 향후 혼합분포모형을 이용한 연구에 좋은 기초자료로 사용될 수 있을 것으로 판단된다.