• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.99-100
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• 2014

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.649-659
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• 2013

We use a Markov chain model to analyze the Korean Baseball League. We derive the distributions of the number of runs scored and the number of batters that complete their turn at bat in a baseball game using the time inhomogeneous Markov chain. The model is tested with real data produced from the 2011 Korean Baseball League.

• Proceedings of the KSPS conference
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• 2005.04a
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• pp.87-90
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• 2005

Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

• Journal of the Korean Statistical Society
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• v.6 no.2
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• pp.97-102
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• 1977

For a sequence of Markov dependent Bernoulli trails, asymptotic normality of the number of success runs with length $r \geq 1$ is established, when the number of trials tends to infinity.

• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.200-207
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• 2014

Average number of samples to signal (ANSS) and average time to signal (ATS) are the most widely used criterion for comparing the efficiencies of the quality control charts. In this study the method of evaluating ANSS and ATS values of the multivariate exponentially weighted moving average (EWMA) control charts with Markov chain approach was presented when the production process is in control state or out of control state. Through numerical results, it is found that when the number of transient state r is less than 50, the calculated ANSS and ATS values are unstable; and ATS(r) tends to be stabilized when r is greater than 100; in addition, when the properties of multivariate EWMA control chart is evaluated using Markov chain method, the number of transient state r requires bigger values when the smoothing constatnt ${\lambda}$ becomes smaller.

• Journal of Biosystems Engineering
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• v.29 no.5 s.106
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• pp.441-450
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• 2004

This study was performed to estimate mid and long term demands of a tractor, a rice transplanter, a combine and a grain dryer by using logistic curve function and Markov chain model. Field survey was done to decide some parameters far logistic curve function and Markov chain model. Ceiling values of tractor and combine fer logistic curve function analysis were 209,280 and 85,607 respectively. Based on logistic curve function analysis, total number of tractors increased slightly during the period analysed. New demand for combine was found to be zero. Markov chain analysis was carried out with 2 scenarios. With the scenario 1(rice price $10\%$ down and current supporting policy by government), new demand for tractor was decreased gradually up to 700 unit in the year 2012. For combine, new demand was zero. Regardless of scenarios, the replacement demand was increased slightly after 2003. After then, the replacement demand is decreased after the certain time. Two analysis of logistic owe function and Markov chain model showed the similar trend in increase and decrease for total number of tractors and combines. However, the difference in numbers of tractors and combines between the results from 2 analysis got bigger as the time passed.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.39-50
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• 1997

Markov chain method to evaluate the properties of control charts with variable sampling intervals(VSI0 for simultaneously monitoring several correlated quality characteristics under multivariate normal process are investigated. For comparing the efficiencies and properties of multivariate control charts, we consider multivariate Shewhart, CUSUM and EWMA charts in terms of average time to signal(ATS) and average number of samples to signal(ANSS). We obtained stabilized numerical results with Markov chain method when the number of transient state is greater than 100.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.1
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• pp.99-103
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• 1975

A procedure for the test of independence of the observations and the null distribution are studied for a waiting-time distribution of the number of Bernoulli trials required to obtain a preassigned number of successes under Markov dependence. Selected critical values for the test statistic are tabulated.

• Industrial Engineering and Management Systems
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• v.15 no.2
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• pp.131-142
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• 2016

We consider a multi-server queueing system without buffer and with two types of customers as a model of operation of a mobile network cell. Customers arrive at the system in the marked Markovian arrival flow. The service times of customers are exponentially distributed with parameters depending on the type of customer. A part of the available servers is reserved exclusively for service of first type customers. Customers who do not receive service upon arrival, can make repeated attempts. The system operation is influenced by random factors, leading to a change of the system parameters, including the total number of servers and the number of reserved servers. The behavior of the system is described by the multi-dimensional Markov chain. The generator of this Markov chain is constructed and the ergodicity condition is derived. Formulas for computation of the main performance measures of the system based on the stationary distribution of the Markov chain are derived. Numerical examples are presented.