• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.261-274
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• 1997

Most of the data sets to which the conventional discriminant rules have been applied contain only those which belong to one and only one class among the classes of interest. However the extension of the bivalence to multivlaence like Fuzzy concepts strongly influence the traditional view that an object must belong to only class. Thus the goal of this paper is to develop new discriminant rules which can handle the data each object of which may belong to moer than two classes with certain degrees of belongings. A calibration model is used for the relationship between the feature vector of an object and the degree of belongings and a Bayesian inference is made with the Metropolis algorithm on the degree of belongings when a feature vector of an object whose membership is unknown is given. An evalution criterion is suggested for the rules developed in this paper and comparision study is carried using two training data sets.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.721-732
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• 2018

Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.477-489
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• 2021

COVID-19 has spread seriously around the world in 2020 and it is still significantly affecting our whole daily life. Currently, the whole world is still undergoing the pandemic and South Korea is no exception to it. During the pandemic, South Korea had several events that prevented or accelerated its spread. To establish the prevention policies for infectious diseases, it is very important to evaluate the intervention effect of such events. The susceptible-infected-removed (SIR) model is often used to describe the dynamic behavior of the spread of infectious diseases through ordinary differential equations. However, the SIR model is a deterministic model without considering the uncertainty of observed data. To consider the uncertainty in the SIR model, the Bayesian approach can be employed, and this approach allows us to evaluate the intervention effects by time-varying functions of the infection rate in the SIR model. In this study, we describe the time trend of the spread of COVID-19 in South Korea and investigate the intervention effects for the events using the stochastic SIR model based on the Bayesian approach.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.476-479
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• 2021

DBMS 파라미터 튜닝이란 데이터베이스에서 제공하는 다양한 파라미터의 값을 조율하여, 최적의 성능을 도출하는 과정이다. 데이터베이스 종류에 따라 파라미터 개수가 수십 개에서 수백 개로 다양하며, 각 기능이 모두 다르기 때문에 최적의 조합을 찾는 것은 쉽지 않다. 선행 연구에서는 BO 기법을 사용하여 적절한 파라미터 값을 추출했지만, 파라미터 개수에 비례하여 차원이 커지는 문제가 발생한다. 본 논문에서는 통계적으로 파라미터를 분류하여 탐색 공간을 줄인 다음 단계적으로 BO 를 수행하는 PBO 방식을 제안한다. 파라미터 값을 랜덤하게 할당하여 벤치마킹한 결과값을 군집화한 후, 각 군집별로 파라미터와의 연관성을 분석해 높은 상관관계를 가진 파라미터를 매칭시켜 분류한다. 제안하는 방법론을 검증하기 위하여 8 가지 회귀 모델과의 비교 실험을 통해 제안한 방법론의 우수성을 검증하였다.

• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.3-13
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• 1999

In this study, an alternative spatial analysis method against conventional methods such as Thiessen method, Inverse Distance method, and Kriging method, named Spatial-Analysis Neural-Network (SANN) is presented. It is based on neural network modeling and provides a nonparametric mean estimator and also estimators of high order statistics such as standard deviation and skewness. In addition, it provides a decision-making tool including an estimator of posterior probability that a spatial variable at a given point will belong to various classes representing the severity of the problem of interest and a Bayesian classifier to define the boundaries of subregions belonging to the classes. In this paper, the SANN is implemented to be used for analyzing a mean annual precipitation filed and classifying the field into dry, normal, and wet subregions. For an example, the whole area of South Korea with 39 precipitation sites is applied. Then, several useful results related with the spatial variability of mean annual precipitation on South Korea were obtained such as interpolated field, standard deviation field, and probability maps. In addition, the whole South Korea was classified with dry, normal, and wet regions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1055-1064
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• 2017

Since a device such as a rocket motor requires very high reliability, a reasonable reliability design process is essential. However, Korea has implemented a design method for applying a safety factor to each component. In classic reliability analysis, input variables such as mean and standard deviation, used in the limit state function, are treated as deterministic values. Because the mean and standard deviation are determined by a small amount of data, this approach could lead to inaccurate results. In this study, reliability analysis is performed for bolted joints and o-ring seals, and the Bayesian approach is used to statistically estimate the input variables. The estimated variables and failure probability, calculated by the reliability analysis, are derived in the form of probability distributions.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.723-733
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• 2009

The crucial problems of K-means clustering are deciding the number of clusters and initial centroids of clusters. Hence, the steps of K-means clustering are generally consisted of two-stage clustering procedure. The first stage is to run hierarchical clusters to obtain the number of clusters and cluster centroids and second stage is to run nonhierarchical K-means clustering using the results of first stage. Here we provide automated K-means clustering procedure to be useful to obtain initial centroids of clusters which can also be useful for large data sets, and provide software program implemented using R.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.477-481
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• 2008

Place recognition is necessary for a mobile user to be provided with place-dependent information. This paper proposes real-time video based place recognition system that identifies users' current place while moving in the building. As for the feature extraction of a scene, there have been existing methods based on global feature analysis that has drawback of sensitive-ness for the case of partial occlusion and noises. There have also been local feature based methods that usually attempted object recognition which seemed hard to be applied in real-time system because of high computational cost. On the other hand, researches using statistical methods such as HMM(hidden Markov models) or bayesian networks have been used to derive place recognition result from the feature data. The former is, however, not practical because it requires huge amounts of efforts to gather the training data while the latter usually depends on object recognition only. This paper proposes a combined approach of global and local feature analysis for feature extraction to complement both approaches' drawbacks. The proposed method is applied to a mobile information system and shows real-time performance with competitive recognition result.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.969-983
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• 2021

Evapotranspiration, one of the hydrometeorological components, is considered an important variable for water resource planning and management and is primarily used as input data for hydrological models such as water balance models. The FAO56 PM method has been recommended as a standard approach to estimate the reference evapotranspiration with relatively high accuracy. However, the FAO56 PM method is often challenging to apply because it requires considerable hydrometeorological variables. In this perspective, the Hargreaves equation has been widely adopted to estimate the reference evapotranspiration. In this study, a set of parameters of the Hargreaves equation was calibrated with relatively long-term data within a Bayesian framework. Statistical index (CC, RMSE, IoA) is used to validate the model. RMSE for monthly results reduced from 7.94 ~ 24.91 mm/month to 7.94 ~ 24.91 mm/month for the validation period. The results confirmed that the accuracy was significantly improved compared to the existing Hargreaves equation. Further, the evaporative demand drought index (EDDI) based on the evaporative demand (E₀) was proposed. To confirm the effectiveness of the EDDI, this study evaluated the estimated EDDI for the recent drought events from 2014 to 2015 and 2018, along with precipitation and SPI. As a result of the evaluation of the Han-river watershed in 2018, the weekly EDDI increased to more than 2 and it was confirmed that EDDI more effectively detects the onset of drought caused by heatwaves. EDDI can be used as a drought index, particularly for heatwave-driven flash drought monitoring and along with SPI.

• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.793-806
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• 2015

The simulation of natural streamflow at ungauged basins is one of the fundamental challenges in hydrology community. The key to runoff simulation in ungauged basins is generally involved with a reliable parameter estimation in a rainfall-runoff model. However, the parameter estimation of the rainfall-runoff model is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of a continuous rainfall-runoff model in conjunction with a Bayesian statistical technique to consider uncertainty more precisely associated with the parameters. First, this study employed Bayesian Markov Chain Monte Carlo scheme for the estimation of the Sacramento rainfall-runoff model. The Sacramento model is calibrated against observed daily runoff data, and finally, the posterior density function of the parameters is derived. Second, we applied a multiple linear regression model to the set of the parameters with watershed characteristics, to obtain a functional relationship between pairs of variables. The proposed model was also validated with gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, index of agreement and the coefficient of correlation.