• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.1-12
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• 1994

We consider the markov process ${X_n}$ on R which is genereated by $X_{n+1} = f(X_n) + \epsilon_{n+1}$. Sufficient conditions for irreducibility and geometric ergodicity are obtained for such Markov processes. In additions, when ${X_n}$ is geometrically ergodic, the functional central limit theorem is proved for every bounded functions on R.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.64-74
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• 2000

This paper investigates the issue of performance testing on web browsing environments. Among the typical non-functional characteristics, index of link validity will be deeply explored. A framework to certify link correctness in web site is proposed. All possible navigation paths are first formulated to represent a usage model with the Markov chain property, which is then used to generate test script file statistically. With collecting any existing failure information followed by tracing these testing browsed paths, certification analysis may be performed by applying Markov chain theory. The certification result will yield some significant information such as: test coverage, reliability measure, confidence interval, etc. The proposed mechanism may provide not only completed but also systemic methodologies to find any linking errors and other web technologies errors. Besides, an actual practice of the proposed approach to a web-based system will be demonstrated quantitatively through a certification tool.

• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1088-1093
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• 2006

In this study, the method of annual sediment estimation for reservoir long-term operation is proposed. Long-term daily precipitation and evaporation are predicted by Markov Chain. Using these values, reservoir inflow is simulated by NWS-PC model. Reservoir sediment load is estimated by sediment rating relation curve which is observed. From the simulation results, it was found that each simulated value by Markov Chain and NWS-PC was well compared to the observed ones and also estimated reservoir sediment was appropriate to the compared values using empirical equations. It is thought that the proposed method for estimation of reservoir sediment can be useful used to operate the reservoir.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.75-85
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• 1997

A stable algorithm for computing the fundamental matrix (I-Q)$^{-1}$ of a Markov chain is proposed, where Q is a substochastic matrix. The proposed algorithm utilizes the GTH algorithm (Grassmann, Taskar and Heyman, 1985) which is turned out to be stable for finding the steady state distribution of a finite Markov chain. Our algorithm involves no subtractions and therefore loss of significant digits due to concellation is ruled out completely while Gaussian elimination involves subtractions and thus may lead to loss of accuracy due to cancellation. We present numerical evidence to show that our algorithm achieves higher accuracy than the ordinagy Gaussian elimination.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.127-132
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• 2008

This project follows the heterogeneous agent market segmented model of Landon-Lane and Occhino (2007) with using Korean data, M1 and GDP deflator from 1882:I to 2007:II. This paper estimates parameters with Monte Carlo Markov Chain. The fraction of traders, ${\lambda}$, in Korea is 15.64%. The quarterly preferences discount factor's, ${\beta}$, posterior mean is 0.9922. The posterior mean of the inverse of the elasticity of the labor supply to the real wage, ${\varphi}$, is 0.0316. The elasticity of the labor supply to the real wage has a very large value. By Hansen (1985) and Christiano and Eichenbaum (1992) and Cooley and Hansen (1989), models having large elasticity of the aggregate labor supply better match macroeconomic data.

• Convergence Security Journal
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• v.14 no.4
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• pp.19-26
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• 2014

Mobile malicious code is typically spread by the worm, and although modeling techniques to analyze the dispersion characteristics of the worms have been proposed, only macroscopic analysis was possible while there are limitations in predicting on certain viruses and malicious code. In this paper, prediction methods have been proposed which was based on Markov chain and is able to predict the occurrence of future malicious code by utilizing the past malicious code data. The average value of the malicious code to be applied to the prediction model of Markov chain model was applied by classifying into three categories of the total average, the last year average, and the recent average (6 months), and it was verified that malicious code prediction possibility could be increased by comparing the predicted values obtained through applying, and applying the recent average (6 months).

• Journal of Wetlands Research
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• v.5 no.2
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• pp.1-13
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• 2003

In Korea, most of the rainfalls have been concentrated in the flood season and the flood study has received more attention than low flow analysis. One of the reasons that the analysis of low flows has less attention is the lacks of the required data like daily rainfall and so we have used the stochastic processes such as pulse noise, exponential distribution, and state model of Markov chain for the rainfall simulation in short term such as daily. Especially this study will pay attention to the state model of Markov chain. The previous study had performed the simulation study by the state model without considerations of the flood and non-flood periods and without consideration of the frequency of rainfall for the period of a state. Therefore this study considers afore mentioned two cases and compares the results with the known state model. As the results, the RMSEs of the suggested and known models represent the similar results. However, the PRE(relative percentage error) shows the suggested model is better results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.135-138
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• 2023

In this paper, we study real-time error in the context of monitoring a binary information source through a delay system. To derive the average real-time error, we model the delay system as a discrete time Geo/D/1/1 queueing model. Using a discrete time three-dimensional Markov chain with finite state space, we analyze the queueing model. We also perform some numerical analysis on various system parameters: state transition probabilities of binary information source; transmission times; and transmission frequencies. When the state changes of the information source are positively correlated and negatively correlated, we investigate the relationship between transmission time and transmission frequency.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.123-123
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• 2012

일강수량은 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로 이용된다. 일반적으로 수자원계획은 장기적인 목적을 가지고 수행되어지며, 장기간의 일강수량 자료를 필요로 한다. 하지만 장기간의 일강수량 자료의 획득의 어려움으로 단기간의 일강수량자료를 이용하여 모의한 장기간 강수자료를 이용하게 된다. 이처럼 수자원계획의 수립에 있어서 일강수량 모의기법의 성능은 수자원계획의 신뢰성 및 결과에 큰 영향을 준다. 일강수량 모의기법은 국내외적으로 매우 활발하게 이루어지고 있으며, 수자원계획 및 수공구조물 설계 외에도 매우 다양한 목적으로 활용되어 지고 있다. 일강수량을 모의기법 중 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이지만, 기존 Markov Chain 모형을 통한 일강수량 모의는 극치강수량을 재현하기 어렵다는 문제점이 있다. 또한, 일강수량 모의 기법의 목적인 수자원계획 및 수공구조물 설계 등의 입력자료로 활용되어지기 위해서는 모의 결과가 유역내 지점별 공간 상관성을 재현함으로써 모형의 우수성과 자료결과의 신뢰성을 확보할 수 있어야 하겠다. 이러한 점에서 본 연구에서는 내삽에서 우수한 재현능력을 갖는 핵 밀도함수와 극치강수량 재현에 유리한 GPD분포의 특징을 함께 고려할 수 있는 불연속 Kernel-Pareto Distribution 기반에 공간상관성 재현 알고리즘을 결합한 일강수량모의기법을 개발하였다. 한강유역의 18개 강수지점에 대해서 기존 Gamma분포를 사용한 Markov Chain 모형과 본 연구에서 제안한 방법을 적용하여 모형을 평가해 보고자 한다. Gamma 분포기반 Markov Chain 모형의 경우 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 적용한 다지점 불연속 Kernel-Pareto 분포 모형은 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하며, 100년빈도 강수량 모의결과 기존 모의모형의 문제점을 보완할 수 있는 개선된 결과를 보여주었다. 본 연구에서 제시한 방법론은 유역내의 공간상관성을 재현하며, 평균 및 중간값 등 낮은 차수의 모멘트 등 일강수량 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.267-274
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• 1999

We consider a doubly stochastically perturbed dynamical system {$X_n$} generated by $X_n\Gamma_n(X_{n-1})+W_n where \Gamma_n$ is a Markov chain of random functions and $W_n$ is i.i.d. random elements. Sufficient conditions for stationarity and geometric ergodicity of $X_n$ are obtained by considering asymptotic behaviours of the associated Markov chain. Ergodic theorem and functional central limit theorem are proved.