• Journal of the Korean Data and Information Science Society
• /
• v.17 no.4
• /
• pp.1329-1341
• /
• 2006

When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

• Communications for Statistical Applications and Methods
• /
• v.6 no.1
• /
• pp.169-180
• /
• 1999

A Markov Chain Monte Carlo method with development of computation is used to be the software system reliability probability model. For Bayesian estimator considering computational problem and theoretical justification we studies relation Markov Chain with Gibbs sampling. Special case of GOS with Superposition for Goel-Okumoto and Weibull models using Gibbs sampling and Metropolis algorithm considered. In this paper discuss Bayesian computation and model selection using posterior predictive likelihood criterion. We consider in this paper data using method by Cox-Lewis. A numerical example with a simulated data set is given.

• Journal of the Korea Safety Management & Science
• /
• v.17 no.1
• /
• pp.169-177
• /
• 2015

We develop a model to predict typhoons in Korea. We collect data for typhoons and classify those depending on the severity level. Following a Bayesian approach, we develop a model that explains the relationship between different levels of typhoons. Through the analysis of the model, we can predict the rate of typhoons, the probability of approaching Korean peninsular, and the probability of striking Korean peninsular. We show that the uncertainty for the occurrence of various types of typhoons reduces dramatically by adaptively updating model parameters as we acquire data.

• Ocean and Polar Research
• /
• v.41 no.3
• /
• pp.183-191
• /
• 2019

This study sought to find which model is most appropriate for estimating potential yield in the East Sea, Republic of Korea. For comparison purposes, the Process-error model, ASPIC model, Maximum entropy model, Observation-error model, and Bayesian state-space model were applied using data from catch amounts and total efforts of the whole catchable fishes in the East Sea. Results showed that the Bayesian state-space model was estimated to be the most reliable among the models. Potential yield of catchable species was estimated to be 227,858 tons per year. In addition, it was analyzed that the amount of fishery resources in 2016 was about 63% of the biomass that enables a fish stock to deliver the maximum sustainable yield.

• Journal of Korea Water Resources Association
• /
• v.47 no.10
• /
• pp.853-865
• /
• 2014

Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship need to be established to quantify various uncertainties associated modeling process and inputs. However, the systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, two major innovations are introduced to address this situation. The first is the use of a Hierarchical Bayesian model based regional frequency analysis to better convey uncertainties associated with the parameters of probability density function to the dam risk analysis. The second is the use of Bayesian model coupled HEC-1 rainfall-runoff model to estimate posterior distributions of the model parameters. A reservoir routing analysis with the existing operation rule was performed to convert the inflow scenarios into water surface level scenarios. Performance functions for dam risk model was finally employed to estimate hydrologic dam risk analysis. An application to the Dam in South Korea illustrates how the proposed approach can lead to potentially reliable estimates of dam safety, and an assessment of their sensitivity to the initial water surface level.

• Journal of Korea Water Resources Association
• /
• v.53 no.5
• /
• pp.383-393
• /
• 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
• /
• v.50 no.11
• /
• pp.745-758
• /
• 2017

The copula-based models have been successfully applied to hydrological modeling including drought frequency analysis and time series modeling. However, uncertainty estimation associated with the parameters of these model is not often properly addressed. In these context, the main purposes of this study are to develop the Bayesian inference scheme for bivariate copula functions. The main applications considered are two-fold: First, this study developed and tested an approach to copula model parameter estimation within a Bayesian framework for drought frequency analysis. The proposed modeling scheme was shown to correctly estimate model parameters and detect the underlying dependence structure of the assumed copula functions in the synthetic dataset. The model was then used to estimate the joint return period of the recent 2013~2015 drought events in the Han River watershed. The joint return period of the drought duration and drought severity was above 100 years for many of stations. The results obtained in the validation process showed that the proposed model could effectively reproduce the underlying distribution of observed extreme rainfalls as well as explicitly account for parameter uncertainty in the bivariate drought frequency analysis.

• Smart Structures and Systems
• /
• v.15 no.2
• /
• pp.395-408
• /
• 2015

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

• Smart Structures and Systems
• /
• v.17 no.3
• /
• pp.445-470
• /
• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.3
• /
• pp.316-321
• /
• 2007

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.