• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.427-438
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• 2013

Based on progressive Type II censored sampling which is an important method to obtain failure data in a lifetime study, we suggest a very general form of Bayesian prediction bounds from two parameters exponentiated Weibull distribution using the proper general prior density. For this, Markov chain Monte Carlo approach is considered and we also provide a simulation study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.389-397
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both Gumbel distribution and trend analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.751-758
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• 2015

Mathematical models are actively used to reduce the experimental expenses required to understand physical phenomena. However, they are different from real phenomena because of assumptions or uncertain parameters. In this study, we present a calibration and validation method using a paper helicopter and statistical methods to quantify the uncertainty. The data from the experiment using three nominally identical paper helicopters consist of different groups, and are used to calibrate the drag coefficient, which is an unknown input parameter in both analytical models. We predict the predicted fall time data using probability distributions. We validate the analysis models by comparing the predicted distribution and the experimental data distribution. Moreover, we quantify the uncertainty using the Markov Chain Monte Carlo method. In addition, we compare the manufacturing error and experimental error obtained from the fall-time data using Analysis of Variance. As a result, all of the paper helicopters are treated as one identical model.

• International Journal of Reliability and Applications
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• v.13 no.1
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• pp.37-47
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• 2012

In this article we attempted reliability analysis of a component under the stress-strength pattern with both classical as well as Bayesian techniques. The main focus is made to develop the theory for dealing the reliability problems in various circumstances for bivariate environmental set up in context of Bayesian paradigm. A stress-strength based model describes the life of a component which has strength (Y) and is subjected to stress(X). We develop the Bayes and moment estimators of reliability of a component for each of the three possible conditions, under the assumption that the two stresses (i.e. $X_1$ and $X_2$) on a component are dependent and follow a Bivariate exponential (BVE) of Marshall-Olkin distribution, the strength of a component (Y) following exponential distribution is independent of the stresses. The simulation study is performed with Markov Chain Monte Carlo technique via Gibbs sampler to obtain the estimates of Bayes estimators of reliability, are compared with moment estimators of reliabilities on the basis of absolute biases.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1275-1283
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• 2015

Markov chains for active tracking which assigns additional track illuminations evenly between search illuminations for a radar system are presented in this article. And some quantitative analyses on track formation range are discussed by using them. Compared with track-while-search (TWS) tracking that uses scan-to-scan correlation at search illuminations for tracking of a target, active tracking has shown the maximum improvement in track formation range of about 27.6%. It is also shown that the number and detection probability of additional track beams have impact on the track formation range. For the consideration of radar resource management at the preliminary radar system design stage, the presented analysis method can be used easily without the need of Monte Carlo simulation.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.413-418
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• 2004

The bounded adjustment is known to be more efficient than repeated adjustment when the cost is incurred for engineering process control. The procedure of the bounded adjustment is to adjust the process when the one-step predicted deviation exceeds the adjustment limit by the amount of the prediction. In this paper, two run rules are proposed and studied in order to improve the efficiency of the traditional bounded adjustment procedure. The efficiency is studied in terms of the standardized cost through Monte Carlo simulation when the procedure is operated with and without the run rules. The adjustment procedure operated with run rules turns out to be more robust for changes in the process and cost parameters. The Markov chain approach for calculating the properties of the run rules is also studied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.667-673
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• 2019

Global warming may accelerate climate change and may increase disaster caused by strong winds. This research studied a method for a nonstationary frequency analysis considering the linear trend over time. The Bayesian method was used to estimate the posterior distribution of the parameters for the extreme value distribution of the annual maximum wind speed at Jeju Airport. The nonstationary frequency analysis was performed based on the Monte Carlo Markov Chain simulation and the Gibbs sampling. The estimated wind speeds by nonstationary frequency analysis was larger than those by stationary analysis. The conventional frequency analysis procedure assuming stationarity is likely to underestimate the future design wind speed in the region where statistically significant trend exists.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1049-1068
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• 2014

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.