• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.226-228
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• 2004

베이지안 네트워크는 불확실한 상황을 모델링하기 위한 확률 기반의 모델이다. 베이지안 네트워크의 구조를 자동 학습하기 위한 연구가 많이 있었고, 최근에는 진화 알고리즘을 이용한 연구가 많이 진행되고 있다. 그러나 대부분은 마지막 세대의 가장 좋은 개체만을 이용하고 있다. 시스템이 요구하는 다양한 요구조건을 하나의 적합도 평가 수식으로 나타내기 어렵기 때문에, 마지막 세대의 가장 좋은 개체는 종종 편향되거나 변화하는 환경에 덜 적응적일 수 있다. 본 논문에서는 적합도 공유 방법으로 다양한 베이지안 네트워크를 생성하고, 이를 베이즈 규칙을 통해 결합하여 변화하는 환경에 적응적인 추론 모델을 구축할 수 있는 방법을 제안한다. 성능 평가를 위해 ALARM 네트워크에서 인공적으로 생성한 데이터를 이용한 구조 학습 및 추론 실험을 수행하였다. 다양한 조건에서 학습된 네트워크를 실험한 결과, 제안한 방법이 변화하는 환경에서 더욱 강건하고 적응적인 모델을 생성할 수 있음을 확인한 수 있었다.

• Survey Research
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• v.9 no.1
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• pp.87-100
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• 2008

We calculated the predictive interval for the number of seats belonging to major political parties in the case of the 2004 General Election of Korea, using Bayesian frame of inference. Moreover, we proposed the adjustment procedure for correcting the minor group's propensity of refusal or nonresponse due to effect of the spiral of silence.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.263-268
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• 1994

Thomas Bayes assumed uniform prior for the location $\theta$ of a billiard ball W in his historic 1764 paper. In this study, following mathematical derivation of the uniform distribution from several assumptions that are plausible on te billiard table, it is argued that the probabilistic meaning of Bayes' uniform prior (especially in Billiard Problem) is not just sujective but logical.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.61-68
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• 2002

통계학은 불확실성(uncertainty)에 대한 연구이다. 베이지안 통계 방법은 불확실성 아래서 통계 추론과 의사 결정 모두를 위한 완전한(complete) 패러다임을 제공한다. 베이지안 방법론은 합리적인 초기 정보와 결합하는 것을 가능하게 만들고, 전통적인 통계적 방법론에 의하여 직면하는 많은 어려움들을 풀 수 있는 coherent 방법론을 제공하면서 엄격한 수학적 기본에 근거하고 있다. 베이지안 패러다임은 일반적인 용어로써 확률이란 단어의 사용을 가장 잘 어울리게 하는 불확실성의 조건부 측도(conditional measure of uncertainty)로써 확률의 해석에 근거한다. 관심있는 것에 대한 통계적 추론은 증거의 관점에서 그 값에 대한 불확실성의 변형으로써 묘사되며, 베이즈 정리(Bayes' theorem)는 이러한 변형이 어떻게 만들어지는 가를 자세히 설명할 수 있다. 베이지안 방법들은 전통적인 통계적 방법론에 접근할 없는 복잡하고, 다양한 구조적 문제들에 응용할 수 있다.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.909-922
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• 2014

This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.

• Korean Journal of Logic
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• v.8 no.1
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• pp.47-67
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• 2005

Bayesian networks have been used in studying and simulating causal inferences by using the probability function distributed over the variables consisting of inquiry space. The focus of the debates concerning Bayesian networks is the causal Markov condition that constrains the probabilistic independence between all the variables which are not in the causal relations. Cartwright, a strong critic about the Bayesian network theory, argues that the causal Markov condition cannot hold in indeterministic systems, so it cannot be a valid principle for causal inferences. The purpose of the paper is to explore whether her argument on the causal Markov condition is valid. Mainly, I shall argue that it is possible for upholders of the causal Markov condition to respond properly the criticism of Cartwright through the continuous causal model that permits the infinite sequence of causal events.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1569-1580
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• 2004

One commonly used approach to deal with uncertainty is Bayesian network which represents joint probability distributions of domain. There are some attempts to team the structure of Bayesian networks automatically and recently many researchers design structures of Bayesian network using evolutionary algorithm. However, most of them use the only one fittest solution in the last generation. Because it is difficult to combine all the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In order to evaluate performance, we conduct experiments on learning Bayesian networks with artificially generated data from ASIA and ALARM networks. According to the experiments with diverse conditions, the proposed method provides with better robustness and adaptation for handling uncertainty.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.481-491
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• 2014

The power production using renewable energy is more important because of a limited amount of fossil fuel and the problem of global warming. A concentrative photovoltaic system comes into the spotlight with high energy production, since the rate of power production using solar energy is proliferated. These systems, however, need to sophisticated tracking methods to give the high power production. In this paper, we propose a hierarchical tracking system using modular Bayesian networks and a naive Bayes classifier. The Bayesian networks can respond flexibly in uncertain situations and can be designed by domain knowledge even when the data are not enough. Bayesian network modules infer the weather states which are classified into nine classes. Then, naive Bayes classifier selects the most effective method considering inferred weather states and the system makes a decision using the rules. We collected real weather data for the experiments and the average accuracy of the proposed method is 93.9%. In addition, comparing the photovoltaic efficiency with the pinhole camera system results in improved performance of about 16.58%.