• Proceedings of the Korean Geotechical Society Conference
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• 1996.03a
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• pp.155-166
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• 1996

• Geotechnical Engineering
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• v.12 no.2
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• pp.43-58
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• 1996

In spite of drastic development of underground technology too many uncertainties still exist in design and construction of underground structures. Estimation of ground parameters might be one of those uncertainties in design of underground structures. It is not an easy task to estimate the parameters reasonably well in advance in the design stage. The main purpose of this paper is the best parameter estimation in the underground structures. In order to estimate unknown model parameters from the in-eitu measurements as well as prior estimates, the Extended Bayesian Method(EBM) is utilized and implemented with Finite Element Program. The parameter estimation model utilized in this study is applied to two underground structures : the one Pusan subway tunnel: and the other Darlington intake tunnel in Canada, and the effectiveness of the proposed model is illustrated.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.5-11
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• 2008

한국어 띄어쓰기에서 통계적 모델을 사용한 기존의 연구들은 최대우도추정(Maximum Likelihood Estimation)에 기반하고 있다. 그러나 최대우도추정은 자료부족 시 부정확한 결과를 주는 단점이 있다. 본 연구는 이에 대한 대안으로 사용자 입력을 고려하는 베이지언 파라미터 추정(Bayesian parameter estimation)을 제안한다. 기존 연구가 사용자 입력을 교정 대상으로만 간주한 것에 비해, 제안 방법은 사용자 입력을 교정 대상이면서 동시에 학습의 대상으로 해석한다. 제안하는 방법에서 사용자 입력은 학습 말뭉치의 자료부족에서 유발되는 부정확한 파라미터 추정(parameter estimation)을 방지하는 역할을 수행하고, 학습 말뭉치는 사용자 입력의 불확실성을 보완하는 역할을 수행한다. 실험을 통해 문어체 말뭉치, 통신환경 구어체 말뭉치, 웹 게시판 등 다양한 종류의 말뭉치와 다양한 통계적 모델에 대해 제안 방법이 효과적임을 알 수 있다.

• Journal of Korean Society for Quality Management
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• v.43 no.1
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• pp.57-66
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• 2015

Purpose: The purpose of this study is to apply the constrained Bayesian estimation methodology for product quality control process and prove the effectiveness of the product management by comparing with the well-known Bayes estimator through data performance result. Methods: The Bayes and constrained Bayes estimators were produced based on the theoretical background and for confirming the effectiveness of suggested application, the deviation index was defined and calculated for the comparison. Results: The statistical analysis result shows that applying the suggested estimation methodology, that is, constrained Bayes estimator improves the effectiveness of the index with regard to reduce the error by matching the first two empirical moments. Conclusion: Considering the advanced Bayesian approaches such as constrained Bayes estimation for the product quality control process, the newly defined deviation index reduces the error for estimating the parameter histogram which is reflected both location and deviation parameters and furthermore various Bayesian perspective approaches seems to be meaningful for managing the product quality control process.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.383-393
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• 2020

The main objective of this study is to provide a robust model for estimating parameters of the Clark unit hydrograph (UH) using the observed rainfall-runoff data in the Soyangang dam basin. In general, HEC-1 and HEC-HMS models, developed by the Hydrologic Engineering Center, have been widely used to optimize the parameters in Korea. However, these models are heavily reliant on the objective function and sample size during the optimization process. Moreover, the optimization process is carried out on the basis of single rainfall-runoff data, and the process is repeated for other events. Their averaged values over different parameter sets are usually used for practical purposes, leading to difficulties in the accurate simulation of discharge. In this sense, this paper proposed a hierarchical Bayesian model for estimating parameters of the Clark UH model. The proposed model clearly showed better performance in terms of Bayesian inference criterion (BIC). Furthermore, the result of this study reveals that the proposed model can also be applied to different hydrologic fields such as dam design and design flood estimation, including parameter estimation for the probable maximum flood (PMF).

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.253-262
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• 2016

A Rating curve is a regression equation of discharge versus stage for a given point on a stream where the stream discharge is measured across the stream channel with a stage and discharge measurement. The curve is generally used to calculate discharge based on the stage. However, the existing approach showed problems in terms of estimating uncertainty associated with regression parameters including the separation parameter for low and high flow. In this regard, this study aimed to develop a new method for the aforementioned problems based on Bayesian approach, which can better estimate the parameter and its uncertainty. In addition, this study used a Bayesian Multi-Segmented (Bayesian M-S) model which is provided a comparison between the existing and proposed scheme. The proposed model showed better results for the parameter estimation than the existing approach, and provided better performance in terms of estimating uncertainty range.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.385-410
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• 2019

This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.821-831
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• 2014

Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Bayesian model based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.183-189
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• 2003

A momentum threshold autoregressive (MTAR) model, a nonlinear autoregressive model, is analyzed in a Bayesian framework. Parameter estimation in the presence of missing data is done by using Markov chain Monte Carlo methods. We also propose simple Bayesian test procedures for asymmetry and unit roots. The proposed method is applied to a set of Korea unemployment rate data and reveals evidence for asymmetry and a unit root.