• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• Journal of Information Management
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• v.20 no.1
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• pp.66-85
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• 1989

The purpose of this study is to verify the following two hypotheses by using a test collection of 3.815 documents on the subject of chemistry. First hypothesis is that a Markov chain model can be used t9 describe and predict authors' movements among subareas of a discipline. Second hypothesis is that a transition matrix of the Markov chain can be applied to describ the intellectual structure of a discipline en the multidimensional space. The results of this study have shown that the Markov chain is a good model to be used to study the movement of korean chemists in 7 subtopics in chemistry and understand the intellectual structure of chemistry.

• International Journal of Highway Engineering
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• v.4 no.4 s.14
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• pp.1-12
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• 2002

Pavement Management System has a special purpose that the rehabilitation strategy applied on pavement should be executable in view of technical and economical point after new pavement open to the traffic. To achieve that purpose, a reliable pavement performance prediction model should be embeded in the system. The object of this study is to develop a probabilistic pavement performance prediction model for evaluating asphalt pavements based on the Markov chain concept. In this paper, methodology of the Markov chain modeling principle is explained, and the application of this model to asphalt pavement is described. As the results, transition matrics for predicting asphalt pavement performance are obtained, and also performance life is estimated quantitatively by this system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.346-349
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• 2003

This paper is intended to assess a system reliability that is changed as time passes. The proposed methodology consists of two steps: (1) predicting probabilities that each component fails at specific time by using a Markov Chain model and (2) calculating reliability of the whole system via Bayesian network. The proposed methodology includes extended Bayesian network model reflecting the case that components are mutually dependent.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.275-282
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• 2010

This paper analyzes a discrete-time bulk-service queue with probabilistic bulk size, where the service process is interrupted by a Markov chain. We study the joint probability generating function of system occupancy and the state of the Markov chain. We derive several performance measures of interest, including average system occupancy and delay distribution.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.169-180
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• 1999

A Markov Chain Monte Carlo method with development of computation is used to be the software system reliability probability model. For Bayesian estimator considering computational problem and theoretical justification we studies relation Markov Chain with Gibbs sampling. Special case of GOS with Superposition for Goel-Okumoto and Weibull models using Gibbs sampling and Metropolis algorithm considered. In this paper discuss Bayesian computation and model selection using posterior predictive likelihood criterion. We consider in this paper data using method by Cox-Lewis. A numerical example with a simulated data set is given.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.915-927
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• 2003

This paper is the generalization of the modified two-level skip-lot sampling plan(MTSkSP1) to n-level. The general formulas of the operating characteristic(OC) function, average sample number(ASN) and average outgoing quality(AOQ) for the plan are derived using Markov chain properties. The operating characteristic curves, average sample numbers and average outgoing qualities of a reference plan, modified two-level, three-level and five-level skip-lot sampling plans are compared.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.1043-1048
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• 2011

W. Choi([1]) identified and characterized the limiting diffusion of this diploid model by defining discrete generator for the rescaled Markov chain. We denote by F the homozygosity and by S the average selection intensity. In this note, we define the Fleming-Viot process with generator of limiting diffusion and provide exact result for the relations of F and S.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.