• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.1167-1176
• /
• 2005

Tweedie (1957a) proposed a method for the analysis of residuals from an inverse Gaussian population paralleling the analysis of variance in normal theory. He called it the analysis of reciprocals. In this paper, we propose a Bayesian model selection procedure based on the fractional Bayes factor for the analysis of reciprocals. Using the proposed model selection procedures, we compare with the classical tests.

• Journal of the Korean Statistical Society
• /
• v.32 no.4
• /
• pp.411-423
• /
• 2003

In this paper we develop a method for calculating a probability that a particular generalized variance is the smallest of all the K multivariate normal generalized variances. The method gives a way of comparing K multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approach for the probability is intractable and thus a Bayesian method is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach. Necessary theory involved in the method and computation is provided.

• Journal of the Korean Statistical Society
• /
• v.28 no.1
• /
• pp.107-124
• /
• 1999

An approximate Bayes criterion for multivariate Behrens-Fisher problem is proposed and examined. Development of the criterion involves derivation of approximate Bayes factor using the imaginary training sample approach introduced by Speigelhalter and Smith (1982). The criterion is designed to develop a Bayesian test, so that it provides an alternative test to other tests based upon asymptotic sampling theory (such as the tests suggested by Bennett(1951), James(1954) and Yao(1965). For the derived criterion, numerical studies demonstrate routine application and give comparisons with the classical tests.

• Earthquakes and Structures
• /
• v.8 no.4
• /
• pp.935-956
• /
• 2015

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2003.03a
• /
• pp.833-840
• /
• 2003

The stability of structure, effectiveness of design and construction are very important factors in soil-structure design. The design-parameter is based on the test through laboratory-test and field-test. There are two ways to obtain the design-parameter. One is to through test, and the other is through relative documents and references. Recently, statistics has been used to get reliable data. In this study, Kriging method as Geostatistics and the theory of Bayesian's inference are used and the design-parameters are obtained. As the result of this study to the design-parameter is reliable and information about soil condition and soil properties in design and construction is easily found.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.11
• /
• pp.31-37
• /
• 2004

• Journal of Communications and Networks
• /
• v.17 no.1
• /
• pp.75-83
• /
• 2015

In mobile ad-hoc networks (MANETs), nodes are mobile in nature. Collaboration between mobile nodes is more significant in MANETs, which have as their greatest challenges vulnerabilities to various security attacks and an inability to operate securely while preserving its resources and performing secure routing among nodes. Therefore, it is essential to develop an effective secure routing protocol to protect the nodes from anonymous behaviors. Currently, game theory is a tool that analyzes, formulates and solves selfishness issues. It is seldom applied to detect malicious behavior in networks. It deals, instead, with the strategic and rational behavior of each node. In our study,we used the dynamic Bayesian signaling game to analyze the strategy profile for regular and malicious nodes. This game also revealed the best actions of individual strategies for each node. Perfect Bayesian equilibrium (PBE) provides a prominent solution for signaling games to solve incomplete information by combining strategies and payoff of players that constitute equilibrium. Using PBE strategies of nodes are private information of regular and malicious nodes. Regular nodes should be cooperative during routing and update their payoff, while malicious nodes take sophisticated risks by evaluating their risk of being identified to decide when to decline. This approach minimizes the utility of malicious nodes and it motivates better cooperation between nodes by using the reputation system. Regular nodes monitor continuously to evaluate their neighbors using belief updating systems of the Bayes rule.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.4111-4124
• /
• 2022

The control rod drive system is critical to the reactor's reliable operation. The performance of its control system and mechanical system will gradually deteriorate because of operational and environmental stresses, thus increasing the reactor's operational risk. Currently there are few researches on the aging-related degradation of the entire control rod drive system. Because it is difficult to quantify the effect of various environmental stresses and establish an accurate physical model when multiple mechanisms superimposed in the degradation process. Therefore, this paper investigates the aging-related degradation of a control rod drive system by integrating Dynamic Object-Oriented Bayesian Network and Hidden Markov Model. Uncertainties in the degradation of the control system and mechanical system are addressed by using fuzzy theory and the Hidden Markov Model respectively. A system which consists of eight control rod drive mechanisms divided into two groups is used to demonstrate the method. The aging-related degradation of the control rod drive system is analyzed by the Bayesian inference algorithm based on the accelerated life test data, and the impact of different operating schemes on the system performance is also investigated. Meanwhile, the components or units that have major impact on the system's performance are identified at different operational phases. Finally, several essential safety measures are suggested to mitigate the risk caused by the system degradation.

• Korean Journal of Logic
• /
• v.8 no.2
• /
• pp.33-58
• /
• 2005

The old evidence problem raises a profound problem to Bayesian theory of confirmation that evidence known prior to a hypothesis explaining it cannot give any empirical support to the hypothesis. The old evidence problem has resisted to a lot of trials to solve it. The purpose of the paper is to solve the old evidence problem by showing that the problem originated from a serious misunderstanding about the Bayesian concept of confirmation. First, I shall make a brief analysis of the problem, and examine critically two typical Bayesian strategies to solve it. Second, I shah point out a misunderstanding commonly found among Bayesian discussions about the old evidence problem, the ignorance of the asymmetry of confirmation in the context of explanation and prediction. Lastly, 1 shall suggest two different concepts of confirmations by using the asymmetry and argue that the concept of confirmation presupposed in the old evidence problem is not a genuine Bayesian concept of confirmation.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2010.03a
• /
• pp.324-337
• /
• 2010

For the development of load and resistance factor design, reliability analysis is required to calibrate resistance factors in the framework of reliability theory. The distribution of measured-to-predicted pile resistance ratio was constructed based on only the results of load tests conducted to failure for the assessment of uncertainty regarding pile resistance and used in the conventional reliability analysis. In other words, successful pile load test (piles resisted twice their design loads without failure) results were discarded, and therefore, were not reflected in the reliability analysis. In this paper, a new systematic method based on Bayesian theory is used to update reliability index of driven steel pile piles by adding more pile load test results, even not conducted to failure, into the prior distribution of pile resistance ratio. Fifty seven static pile load tests performed to failure in Korea were compiled for the construction of prior distribution of pile resistance ratio. Reliability analyses were performed using the updated distribution of pile resistance ratio and the total load distribution using First-order Reliability Method (FORM). The challenge of this study is that the distribution updates of pile resistance ratio are possible using the load test results even not conducted to failure, and that Bayesian update are most effective when limited data are available for reliability analysis or resistance factors calibration.