• 한국물환경학회지
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• 제40권4호
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• pp.179-194
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• 2024

Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.755-762
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• 2013

본 논문은 NHPP 소프트웨어 신뢰성모형에서 모수추정과 고장시간에 대한 예측을 다루고자 한다. 소프트웨어 신뢰성모형 Goel-Okumoto모형에서 평균값 함수에 대한 최우추정과 경험적 사전분포를 가정한 공액사전분포에서 베이지안 추정을 다루었다. 실제 자료에서 두 가지 추정법에 의한 모수 추정값을 제공하였으며, 모형의 적합성을 판정하고, 고장수에 대한 예측값을 비교하였다.

• 한국환경과학회지
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• 제28권1호
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• 한국물환경학회지
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• 제36권4호
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• pp.300-313
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• 2020

The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized Likelihood Uncertainty Estimation (GLUE) method. These two methods were applied to the TANK model, a hydrological model comprising 13 parameters, to examine the uncertainty of the parameters of the model. The TANK model comprises a combination of multiple reservoir-type virtual vessels with orifice-type outlets and implements a common major hydrological process using the runoff calculations that convert the rainfall to the flow. As a result of the application to the Nam River A watershed, the two Bayesian methods yielded similar flow simulation results even though the parameter estimates obtained by the two methods were of somewhat different values. Both methods ensure the model's prediction accuracy even when the observed flow data available for parameter estimation is limited. However, the prediction accuracy of the model using the MH algorithm yielded slightly better results than that of the GLUE method. The flow duration curve calculated using the limited observed flow data showed that the marginal reliability is secured from the perspective of practical application.

• Computational Structural Engineering : An International Journal
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• 제1권2호
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• pp.81-87
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• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• 디지털융복합연구
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• 제13권10호
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• pp.353-358
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• 2015

어휘 인식 시스템은 학습 모델을 구성하여 인식하므로 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타난다. 이런 경우 인식 모델을 확장할 수 있도록 재구성하거나 인식 모델 구성 시 확장성을 반영하므로 해결할 수 있다. 본 논문에서는 모델 구성 시 확장성을 반영할 수 있는 모수 추정을 위한 베이시안 기법을 사용하여 바타차랴 알고리즘 음성 인식 학습 모델 구성 방법을 융합하여 제안하였다. 음소가 갖는 특징을 기반으로 학습 데이터의 음소에 모수 추정을 위한 베이시안 기법을 이용하였고 유사한 학습 모델은 바타챠랴 알고리즘을 이용하여 정확한 학습 모델로 인식하도록 하였다. 바타챠랴 알고리즘 인식 모델을 구성하여 인식 성능을 평가하였다. 본 논문에서 제안한 시스템을 적용한 결과 어휘 인식률에서 97.5%의 인식률과 1.2초의 학습 시간을 나타내었다.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.1055-1065
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• 2003

We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with exponential lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• 제15권1호
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• pp.239-250
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• 2004

We shall propose maximum likelihood, Bayesian and generalized maximum likelihood estimation for the reliability of the two-unit hot standby system with Rayleigh lifetime distribution that switch is perfect. Each estimation will be compared numerically in terms of various mission times, parameter values and asymptotic relative efficiency through Monte Carlo simulation.