• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.485-490
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• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.275-278
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• 2006

멀티 에이전트(Multi-Agent)들이 상호 연동하여 공통의 목적을 수행하기 위해서는 에이전트를 관리하는 매니지먼트 에이전트(Management Agent)가 요구되고, 주어진 환경에서 획득한 제한된 지식을 효율적으로 이용하는 방법이 필요하다. 본 논문에서는 네이브 베이즈 이론을 적용하여 각 에이전트의 속성값(Attribute Value)에 따라 매니지먼트 에이전트가 각 에이전트를 효율적으로 관리할 수 있는 NBMA(Naive Bayes Management Agent)를 제안하고 이를 이용한 미팅 참가 결정 에이전트를 제안한다. NBMA는 고유한 속성을 지닌 여러 개의 하위 에이전트와 그들을 관리하는 매니지먼트 에이전트로 구성되어 있으며 매니지먼트 에이전트는 하위 에이전트들의 고유한 속성에 대한 메타지식을 이용하여 관리 하도록 한다. 하위 에이전트간에는 상호 조건부 독립(mutually conditional independence) 가정하에 복수의 속성값을 취하며 이러한 속성값에 따라 매니지먼트 에이전트가 조정과 의사결정을 하도록 한다.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.71-87
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• 2014

The Bayes premium or the net premium in the credibility theory does not reflect the underlying tail risk. In this study we examine how the tail risk measures can be utilized in determining the risk premium. First, we show that the risk measures can not only provide the proper risk loading, but also allow the insurer to avoid the wrong decision made with the Bayesian premium alone. Second, it is illustrated that the rank of the tail thickness among different conditional loss distributions does not preserve for the corresponding predictive distributions, even if they share the identical prior variable. The implication of this result is that the risk loading for a contract should be based on the risk measure of the predictive loss distribution not the conditional one.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.275-276
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• 2009

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.377-387
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• 1998

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced latent variables that indicates with component of the Superposition model. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Metropolis algorithms along with Gibbs steps are proposed to preform the Bayesian inference of such models. for model determination, we explored the Pre-quential conditional predictive Ordinate(PCPO) criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions, we consider in this paper Superposition of Musa-Okumoto and Erlang(2) models. A numerical example with simulated dataset is given.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.77-88
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• 2008

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• Environmental and Resource Economics Review
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• v.29 no.1
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• pp.47-89
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• 2020

We introduce a simple theoretical model to analyze the welfare impact of a competition policy in the natural gas market in South Korea. An incumbent monopolistic firm currently owns essential facilities, but the competition policy mandates that the firm provide open access to any entrant firm, charging an access fee. When no regulation is imposed on the fee pricing, this policy increases social welfare as well as the profit of the incumbent firm. When the pricing is regulated, however, social welfare depends on whether there is information asymmetry between the government and the firm regarding the operating cost of the facilities. If the government has complete information, social welfare can be maximized by choosing the optimal prices. Otherwise, the government has to set the prices based on the information that the firm delivers. We formulate a Bayesian game to analyze this case and identify a set of perfect Bayesian equilibria to compare social welfare.

• Convergence Security Journal
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• v.19 no.2
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• pp.163-174
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• 2019

RAM analysis determines the range of resources to be invested by presenting the development goals of the weapon system. If the RAM analysis is not performed properly, it can cause a huge increase in business costs. While the cumulative cost ratio in the concept study is less than 1% of the total cost, 65-70% of the total lifecycle cost is determined and can't be reduced later. Therefore, RAM analysis is crucial in precedent study. When calculating the target RAM value by writing an existing OMS/MP, new functions and the future missions are hardly considered and reflected. Therefore, this paper proposes a method to establish OMS/MP by deriving arguments based on Delphi and Bayesian theory focusing on unmanned combat vehicle.

• Journal of Digital Contents Society
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• v.2 no.2
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• pp.103-115
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• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.