• Title/Summary/Keyword: Gibbs sampling

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Residential Heating Fuel Choice in Korea - A Multinomial Probit Analysis - (Multinomial Probit 모형을 이용한 가정용 난방연료 선택에 관한 연구)

  • Kim, Yeonbae;Shin, Seong-Yun
    • Environmental and Resource Economics Review
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    • v.11 no.4
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    • pp.609-632
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    • 2002
  • 국민소득이 빠르게 증가함에 따라 1990년대 이후 가정용 난방연료의 소비구조 역시 크게 변화하고 있다. 본 연구는 에너지 및 교통수요분석에 많이 사용되는 Multinomial Probit 모형을 이용하여 가정용 난방연료의 선택 행태를 분석하였다. 모형의 추정방법으로는 베이지안(Baysian) 방법론에 의한 Gibbs Sampling기법 (McColluch et al., 2000)을 이용하여 Multinomial probit 모형에서 선택대안이 3개 이상일 경우 발생할 수 있는 추정상의 어려움을 극복하였다. 한국가구패널조사(KHPS) 자료를 이용하여 서울과 경기도 대도시 지역을 대상으로 분석한 결과, 석유와 천연가스가 연탄에 비해 더 밀접한 상호 대체관계를 가지고 있는 것으로 나타났다. 또한 소득이 높은 가구일수록 천연가스에 대한 선호도가 더 높은 것으로 나타나서 향후 공급망 확대에 따라 난방연료용 가스 소비가 더욱 늘어날 것으로 예상된다.

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Bayesian Analysis of Software Reliability Growth Model with Negative Binomial Information (음이항분포 정보를 가진 베이지안 소프트웨어 신뢰도 성장모형에 관한 연구)

  • Kim, Hui-Cheol;Park, Jong-Gu;Lee, Byeong-Su
    • The Transactions of the Korea Information Processing Society
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    • v.7 no.3
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    • pp.852-861
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    • 2000
  • Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

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The NHPP Bayesian Software Reliability Model Using Latent Variables (잠재변수를 이용한 NHPP 베이지안 소프트웨어 신뢰성 모형에 관한 연구)

  • Kim, Hee-Cheul;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.6 no.3
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    • pp.117-126
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    • 2006
  • Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

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Bayesian Analysis and Mapping of Elderly Korean Suicide Rates (베이지안 모형을 활용한 국내 노인 자살률 질병지도)

  • Lee, Jayoun;Kim, Dal Ho
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.325-334
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    • 2015
  • Elderly suicide rates tend to be high in Korea. Suicide by the elderly is no longer a personal problem; consequently, further research on risk and regional factors is necessary. Disease mapping in epidemiology estimates spatial patterns for disease risk over a geographical region. In this study, we use a simultaneous conditional autoregressive model for spatial correlations between neighboring areas to estimate standard mortality ratios and mapping. The method is illustrated with cause of death data from 2006 and 2010 to analyze regional patterns of elderly suicide in Korea. By considering spatial correlations, the Bayesian spatial models, mean educational attainment and percentage of the elderly who live alone was the significant regional characteristic for elderly suicide. Gibbs sampling and grid method are used for computation.

Adaptive Reconstruction of NDVI Image Time Series for Monitoring Vegetation Changes (지표면 식생 변화 감시를 위한 NDVI 영상자료 시계열 시리즈의 적응 재구축)

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.25 no.2
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    • pp.95-105
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    • 2009
  • Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series including bad or missing observation that result from mechanical problems or sensing environmental condition. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) image was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series from 1996 to 2000 for tracking changes on the ground vegetation. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

Topic Modeling on Research Trends of Industry 4.0 Using Text Mining (텍스트 마이닝을 이용한 4차 산업 연구 동향 토픽 모델링)

  • Cho, Kyoung Won;Woo, Young Woon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.7
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    • pp.764-770
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    • 2019
  • In this research, text mining techniques were used to analyze the papers related to the "4th Industry". In order to analyze the papers, total of 685 papers were collected by searching with the keyword "4th industry" in Korea Journal Index(KCI) from 2016 to 2019. We used Python-based web scraping program to collect papers and use topic modeling techniques based on LDA algorithm implemented in R language for data analysis. As a result of perplexity analysis on the collected papers, nine topics were determined optimally and nine representative topics of the collected papers were extracted using the Gibbs sampling method. As a result, it was confirmed that artificial intelligence, big data, Internet of things(IoT), digital, network and so on have emerged as the major technologies, and it was confirmed that research has been conducted on the changes due to the major technologies in various fields related to the 4th industry such as industry, government, education field, and job.

Estimation of Genetic Parameters for Gestation Length, Wean to First Service, Litter Size and Stillborn Piglets in a Closed Nucleus Swine Breeding Herd (특정 종돈집단의 임신기간, 이유후초종부일, 총산자수 및 사산에 대한 유전모수 추정)

  • Lee, Deukhwan;Son, Jihyun
    • Journal of Animal Science and Technology
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    • v.55 no.5
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    • pp.389-398
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    • 2013
  • This study was conducted to investigate the genetic relationships among four reproductive traits. Data for this study were 7616 records from 1910 Landrace (L) and 10,454 records from 2283 Yorkshire (Y) in a closed nucleus swine herd. Traits considered on this study were gestation length (GL), total number of piglets born (TNB), wean to first service (WFS), and number of stillborn per litter (NSB). Heritabilities and genetic correlations were estimated by using the Bayesian inferences via Gibbs sampling in a four trait linear-threshold repeatability animal mixed model by designating NSB as a categorical trait in the L and Y purebred populations. Effects on the statistical model were considered for parity, contemporary group as fixed and service sire, permanent environmental, animal additive genetic effects as random. Estimates of heritability were 0.21, 0.23, 0.16, and 0.09 for GL, WFS, TNB, and NSB in the L population and 0.35, 0.16, 0.14 and 0.10 for corresponding traits in the Y population, respectively. Genetic correlation for GL was -0.59 and -0.28 with TNB and -0.58 and -0.17 with NSB in the L and Y populations, respectively. The NSB was positively correlated with TNB in the L and Y populations in genetic and environmental aspects. Therefore, the NSB should be taken into account in selecting sows for improving prolificacy of dam line breeding swine stock.

Bayesian quantile regression analysis of private education expenses for high scool students in Korea (일반계 고등학생 사교육비 지출에 대한 베이지안 분위회귀모형 분석)

  • Oh, Hyun Sook
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.6
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    • pp.1457-1469
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    • 2017
  • Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

Bayesian Interval Estimation of Tobit Regression Model (토빗회귀모형에서 베이지안 구간추정)

  • Lee, Seung-Chun;Choi, Byung Su
    • The Korean Journal of Applied Statistics
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    • v.26 no.5
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    • pp.737-746
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    • 2013
  • The Bayesian method can be applied successfully to the estimation of the censored regression model introduced by Tobin (1958). The Bayes estimates show improvements over the maximum likelihood estimate; however, the performance of the Bayesian interval estimation is questionable. In Bayesian paradigm, the prior distribution usually reflects personal beliefs about the parameters. Such subjective priors will typically yield interval estimators with poor frequentist properties; however, an objective noninformative often yields a Bayesian procedure with good frequentist properties. We examine the performance of frequentist properties of noninformative priors for the Tobit regression model.

A Bayesian Test for First Order Autocorrelation in Regression Errors : An Application to SPC Approach (회귀모형 오차항의 1차 자기상관에 대한 베이즈 검정법 : SPC 분야에의 응용)

  • Kim, Hea-Jung;Han, Sung-Sil
    • Journal of Korean Society for Quality Management
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    • v.24 no.4
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    • pp.190-206
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    • 1996
  • In case measurements are made on units of production in time order, it is reasonable to expect that the measurement errors will sometimes be first order autocorrelated, and a technique to test such autocorrelation is required to give good control of the productive process. Tool-wear process provide an example for which regression model can sometimes be useful in modeling and controlling the process. For the control of such process, we present a simple method for testing first order autocorrelation in regression errors. The method is based on Bayesian test method via Bayes factor and derived by observing that in general, a Bayes factor can be written as the product of a quantity called the Savage-Dickey density ratio and a correction factor ; both terms are easily estimated from Gibbs sampling technique. Performance of the method is examined by means of Monte Carlo simulation. It is noted that the test not only achieves satisfactory power but eliminates the inconvenience occurred in using the well-known Durbin-Watson test.

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