• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.527-531
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• 2008

For statistical modeling, the model parameters are usually estimated by maximizing a probability measure, such as the likelihood or the posterior. In contrast, a variational Bayesian method treats the parameters of a model as probability distributions and computes optimal distributions for them rather than values. It has been shown that this approach effectively avoids the overfitting problem, which is common with other parameter optimization methods. This paper applies a variational Bayesian technique to surface fitting for height field data. Then, we propose point cloud denoising based on the basic surface fitting technique. Validation experiments and further tests with scan data verify the robustness of the proposed method.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.97-107
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• 2001

A simultaneous test criterion for multiple hypotheses concerning comparison of two multivariate normal populations is considered by using the so called Bayes factor method. Fully parametric frequentist approach for the test is not available and thus Bayesian criterion is pursued using a Bayes factor that eliminates its arbitrariness problem induced by improper priors. Specifically, the fractional Bayes factor (FBF) by O'Hagan (1995) is used to derive the criterion. Necessary theories involved in the derivation an computation of the criterion are provided. Finally, an illustrative simulation study is given to show the properties of the criterion.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.285-295
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• 2006

As more generic drugs become available, the quality, safety, and efficacy of generic drugs have become a public concern. Specifically, drug interchangeability among generic copies of the same brand-name drug is a safety concern. This research proposes a Bayesian method for assessing bioequivalence between two generic copies of the same brand-name drug from two independent $2{\times}2$ crossover design experiments. Uninformative priors are considered for general use and the posterior distribution of the difference of two generic drug effects is derived from which the highest probability density interval can be evaluated. Examples are presented for illustration.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.3
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• pp.291-297
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• 2016

In this paper, we are comparative analysis of exit algorithm and proposal algorithm for desired target direction of arrival estimation in correlation signal system. Proposed algorithm in this paper is to decrease target direction of arrival an estimation error probability using bayesian, maximum posterior, and MUSIC algorithm in order to decrease direction of arrival error probability as optimize and use linear constrained minimum variance to update weight value. Through simulation, we were comparative analysis proposed algorithm and exit MUSIC algorithm. In case SNR is 10dB and antenna element arrays are 9 and 12, We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 11% and 13%, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.69-74
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• 2015

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.405-414
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• 2002

We consider the problem of estimating the error variance of in a two-way mixed-effects ANOVA model using noninformative priors. First, we derive Jeffreys' prior, a reference prior, and matching priors. We then provide marginal posterior distributions under those noninformative priors. Finally, we provide graphs of marginal posterior densities of the error variance and credible intervals for the error variance in two real data set and compare these credible intervals.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.705-715
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• 2014

Metabolic syndrome is a major factor for cardiovascular disease that can develop into a variety of complications such as stroke disease. This study utilizes a Bayesian network to model metabolic syndrome. In addition, we tried to find the best risk combinations to diagnose metabolic syndrome. We confirmed that the combinations are difference according to individual characteristics. The paper used data from 4,489 adults who responded to all health interview questions from the the $5^{th}$ Korea National Health and Nutrition Examination Survey conducted in 2010.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05b
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• pp.7-13
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• 1998

A methodology to analyze and quantify regional meteorological drough based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network ISANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought serverities. For example, the proposed methodology has been applied to analyze the regional drought of South Korea. This is a new method to classify and quantify the spatial or regional drought based on neural network pattern recognition technique and the results show that it may be apprepriate and valuable to analyze the spatial drought.