• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.1-13
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• 2021

The Bayesian method is a very useful statistical tool in various fields including water resources. Therefore, in this study, the background of the Bayesian statistics and its application to the water resources field are reviewed. First, the history of the Bayesian method from the birth to the present, and the achievements of Bayesian statisticians are summarized. Next, the derivation of the Bayes' theorem, which is the basis of the Bayesian method, is presented, and the roles of the three elements of the Bayes' theorem: priori distribution, likelihood function, and posteriori distribution are explained. In addition, the unique features and advantages of the Bayesian statistics are summarized. Finally, the cases in water resources where the Bayesian method is applied are summarized by dividing them into several categories. With a prevalence of information and big data in the future, the Bayesian method is expected to be used more actively in the water resources field.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.523-526
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• 2015

In recent years, building commissioning has often been part of a standard delivery practice in construction, particularly in the high-performance green building market, to ensure the building is designed and constructed per owner's requirements. Commissioning, therefore, intends to provide quality assurance that buildings perform as intended by the design and often helps achieve energy savings. Commissioning, however, is not as widely adopted as its potential benefits are perceived. Owners are still skeptical of the cost-effectiveness claims by energy management and commissioning professionals. One of the issues in the current commissioning practice is that not every project is guaranteed to benefit from the commissioning services. This, coupled with its added cost, the commissioning service is not acquired with great acceptance and confidence by building owners. To overcome this issue, this paper presents a unique methodology to enhance owner's predicting capability of the degree of success of commissioning service using the Bayesian theorem. The paper analyzes a situation where a future building owner wants to use a pre-commissioning in an attempt to refine the success rate of the future commissioned building performance. The author proposes the Bayesian theorem based framework to improve the current commissioning practice where building owners are not given accurate information how much successful their projects are going to be in terms of energy savings from the commissioning service. What should be provided to the building owners who consider their buildings to be commissioned is that they need some indicators how likely their projects benefit from the commissioning process. Based on this, the owners can make better informed decisions whether or not they acquire a commissioning service.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.61-69
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• 2001

We present a probabilistic reasoning method for inferring knowledge about mathematical truth before an automated theorem prover completes a proof. We use a Bayesian analysis to update beleif in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.297-306
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• 2018

Cardiovascular disease (CVD) is the leading cause of death worldwide and has a high mortality rate after onset; therefore, the CVD management requires the development of treatment plans and the prediction of prevalence rates. In our study, age, income, education level, marriage status, diabetes, and obesity were identified as risk factors for CVD. Using these 6 factors, we proposed a nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model for CVD. The attributes for each factor were assigned point values between -100 and 100 by Bayes' theorem, and the negative or positive attributes for CVD were represented to the values. Additionally, the prevalence rate can be calculated even in cases with some missing attribute values. A receiver operation characteristic (ROC) curve and calibration plot verified the nomogram. Consequently, when the attribute values for these risk factors are known, the prevalence rate for CVD can be predicted using the proposed nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model.

• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.164-168
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• 2003

Bayes theorem, suggested by the British Mathematician Bayes (18th century), enables the prior estimate of the probability of an event under the condition given by a specific This theorem has been frequently used to revise the failure probability of a component or system. 2-Stage Bayesian procedure was firstly published by Shultis et al. (1981) and Kaplan (1983), and was further developed based on the studies of Hora & Iman (1990) Papazpgolou et al., Porn(1993). For a small observed failure number (below 12), the estimated reliability of a system or component is not reliable. In the case in which the reliability data of the corresponding system or component can be found in a generic reliability reference book, however, a reliable estimation of the failure probability can be realized by using Bayes theorem, which jointly makes use of the observed data (specific data) and the data found in reference book (generic data).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.6
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• pp.521-528
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• 2021

In order to allocate an appropriate interceptor weapon to an air track for which the threat assessment has been completed, it is necessary to evaluate the suitability of engagement in consideration of the expected point of engagement. In this thesis, a method of calculating the kill probability is proposed according to the position in the engagement space using Bayesian theorem with multivariate attribute information such as relative distance, approach azimuth angle, and altitude of the air track when passing through the engagement space. As a result of the calculation, it was confirmed that the distribution form of the kill probability value for each point in the engagement space follows a multivariate normal distribution based on the optimal predicted intercepting point. It is expected to be applicable to the engagement suitability evaluation of the engagement space.

• Smart Structures and Systems
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• v.22 no.2
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• pp.193-200
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• 2018

From the viewpoint of engineering applications, the prediction of the failure of bogies plays an important role in preventing the occurrence of fatigue. Fatigue is a complex phenomenon affected by many uncertainties (such as load, environment, geometrical and material properties, and so on). The key to predict fatigue damage accurately is how to quantify these uncertainties. A Bayesian model is used to account for the uncertainty of various sources when predicting fatigue damage of structural components. In spite of improvements in the design of fatigue-sensitive structures, periodic non-destructive inspections are required for components. With the help of modern nondestructive inspection techniques, the fatigue flaws can be detected for bogie structures, and fatigue reliability can be updated by using Bayesian theorem with inspection data. A practical fatigue analysis of welded bogies is utilized to testify the effectiveness of the proposed methods.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.852-859
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• 2019

With the development of IT technology and smart devices, various applications utilizing image information are being developed. In order to provide an intuitive interface for pronunciation recognition, there is a growing need for research on pronunciation recognition using mouth feature values. In this paper, we propose a system to distinguish Korean vowel pronunciations by detecting feature points of lips region in images and applying Bayesian based learning model. The proposed system implements the recognition system based on Bayes' theorem, so that it is possible to improve the accuracy of speech recognition by accumulating input data regardless of whether it is speaker independent or dependent on small amount of learning data. Experimental results show that it is possible to effectively distinguish Korean vowels as a result of applying probability based Bayesian classification using only visual information such as mouth shape features.

• Journal of the Korean Statistical Society
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• v.34 no.3
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• pp.235-244
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• 2005

In recent years, theoretical properties of Bayesian nonparametric survival models have been studied and the conclusion is that although there are pathological cases the popular prior processes have the desired asymptotic properties, namely, the posterior consistency and the Bernstein-von Mises theorem. In this study, through a simulation experiment, we study the finite sample properties of the Bayes estimator and compare it with the frequentist estimators. To our surprise, we conclude that in most situations except that the prior is highly concentrated at the true parameter value, the Bayes estimator performs worse than the frequentist estimators.

• IE interfaces
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• v.6 no.2
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• pp.79-89
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• 1993

We develop a statistical model to describe nuclear power plant accidents and predict time to next accident of various levels. We adopt Bayesian approach to obtain posterior and predictive distributions for the time to next accident. We also derive an approximation method to solve many dimensional numerical integration problems that we often encounter in a Bayesian approach. We introduce Influence Diagrams in modeling, and parameter updating, thereby the dependency or independency among model parameters are clearly shown. Also Separable Updating Theorem is utilized to easily obtain the posterior distributions.