• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.71-80
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• 2006

A nonparametric Bayesian multiple comparisons problem (MCP) for dependence parameters in I bivariate exponential populations is studied here. A simple method for pairwise comparisons of these parameters is also suggested. Here we extend the methodology studied by Gopalan and Berry (1998) using Dirichlet process priors. The family of Dirichlet process priors is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of MCP for the dependent parameters of bivariate exponential populations is illustrated through a numerical example.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate 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. A numerical example with simulated data set is given.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.919-929
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• 2013

We compared two basic methods, combine-accumulate method and accumulate-combine method, using the past quality information in multivariate quality control procedure for monitoring mean vector of multivariate normal process. When small or moderate shifts have occurred, accumulate-combine method yields smaller average run length (ARL) and average time to signal (ATS) than combine-accumulate method. On the other hand, we have found from our numerical results that combine-accumulate method has better performances in terms of switching behavior than accumulate-combine method. In industry, a quality engineer could select one of the two method under the comprehensive consideration about the required time to signal, switching behavior, and other physical factors in the production process.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.142-148
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• 2011

Because currently available wireless technologies are not appropriate for wireless body area networks (WBANs), the IEEE 802.15.6 standard was introduced by the IEEE 802.15.6 Task Group to satisfy all the requirements for a monitoring system that operates on, in, or around the human body. In this work, we develop an analytical model for evaluating the performance of an IEEE 802.15.6-based WBAN under saturation condition and a noisy channel. We employ a three-dimensional Markov chain to model the backoff procedure as specified in the standard. Probability generating functions (PGFs) are used to compute the performance descriptors of the network. The results obtained from the analytical model are validated by simulation results. Our results indicate that under saturation condition, the medium is accessed by the highest user priority nodes at the vast majority of time while the other nodes are starving.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.289-301
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• 2002

This paper considers the Bayesian analysis of the regression model wish autoregressive errors. The Bayesian approach for finding the order p of autoregressive error is proposed and the proposed method can be simplified by generalized Savage-Dicky density ratio(Verdinelli and Wasser-man, [18]). And the Markov chain Monte Carlo method(Gibbs sample, [7]) is used in order to overcome the difficulty of Bayesian computations. Final1y, several examples are used to illustrate our proposed methodology.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.243-248
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• 2013

Reliability evaluation is important task in embedded system. It can avoid potential failures and manage the vulnerable components of embedded system effectively. Dynamic fault tree analysis is one of the reliability evaluation methods. It can represent dynamic characteristics of a system such as fault & error recovery, sequence-dependent failures. In this paper, the steering system, which is embedded system in vehicles, is represented using dynamic fault tree. We evaluate the steering system using approximation algorithm based on Simpson's rule. A set of simulation results shows that proposed method overcomes the low accuracy of classic approximation method without requiring no excessive calculation time of the Markov chain method.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1453-1456
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• 2010

본 연구에서는 권역별 일강수량에 대한 기후변화 영향을 평가하고자 한다. 이를 위해서 지역기후 모형으로부터 유도된 A2시나리오가 기본자료로서 활용되며 분석에 앞서 기후변화 시나리오의 편의를 보정하였다. 보정된 지역기후 시나리오는 비정상성 Markov Chain 모형을 통해 강수지점별로 상세수문시나리오로 가공되어 분석에 이용되었다. 강수량에 대한 연주기 분포에 대한 특성을 권역별로 시기별로 평가하였다. 연주기의 분포는 관측치와 유사한 거동을 보이고 있으며 강우량이 2075년대에 증가하는 것으로 나타났다. 기후변화에 따른 일강수량에 발생 특성을 평가하기 위해서 Dryspell과 Wetspell을 지속시간분포별로 추정하여 분석하였다. 전체적으로 Dryspell이 지속시간별로 증가하는 것으로 나타나고 있으며 무강수일수의 증가도 전망되고 있어 같은 연강수량이라도 변동성이 크게 나타날 수 있는 개연성이 크다 하겠다. 일강수량, 월강수량, 연강수량에 대한 다양한 분석이 수행되었으며 기후변화에 따른 강수량의 변동양상을 권역별로 평가하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.535-542
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• 2013

Coordinated multi-point transmission (CoMP) is considered to be a promising technique to improve the throughput for LTE-Advanced systems. One important approach for CoMP is Joint Transmission (JT). However, the analytical model of JT has not been fully studied, as user equipments (UEs) receiving the desired signals from an adjacent base station (BS) as well as serving BS, or only serving BS were not distinguished. We derive a new analytical model to describe the call admission control in JT based systems. The performance measures of interest are the call blocking probability, and resource utilization. Furthermore, we compare the performance of JT-based systems and non-JT- based systems. The analytical results are in reasonable agreement with the simulation results.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.851-858
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• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.