• Title, Summary, Keyword: Gibbs sampler

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Estimation of Genetic Parameter for Carcass Traits According to MTDFREML and Gibbs Sampling in Hanwoo(Korean Cattle) (MTDFREML 방법과 Gibbs Sampling 방법에 의한 한우의 육질형질 유전모수 추정)

  • 김내수;이중재;주종철
    • Journal of Animal Science and Technology
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    • v.48 no.3
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    • pp.337-344
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    • 2006
  • The objective of this study was to compare of genetic parameter estimates on carcass traits of Hanwoo(Korean Cattle) according to modeling with Gibbs sampler and MTDFREML. The data set consisted of 1,941 cattle records with 23,058 animals in pedigree files at Hanwoo Improvement Center. The variance and covariance among carcass traits were estimated via Gibbs sampler and MTDFREML algorithms. The carcass traits considered in this study were longissimus dorsi area, backfat thickness, and marbling score. Genetic parameter estimates using Gibbs sampler and MTDFREML from single-trait analysis were similar with those from multiple-trait analysis. The estimated heritabilities using Gibbs sampler were .52~.54, .54 ~.59, and .42~.44 for carcass traits. The estimated heritabilities using MTDFREML were .41, .52~.53, and .31~.32 for carcass traits. The estimated genetic correlation using Gibbs sampler and MTDFREML of LDA between BF and MS were negatively correlated as .34~.36, .23~.37. Otherwise, genetic correlation between BF and MS was positive genetic correlation as .36~.44. The correlations of breeding value for marbling score between via MTDFREML and via Gibbs sampler were 0.989, 0.996 and 0.985 for LDA, BF and MS respectively.

Reliability of the Mixture Model with Gamma Family Using Gibbs Sampler (깁스추출법을 이용한 감마족 신뢰확률 혼합모형에 대한 연구)

  • 김평구
    • Journal of the Korean Society for Quality Management
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    • v.27 no.1
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    • pp.80-90
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    • 1999
  • In this paper, reliability estimation using Gibbs sampler is considered for the mixture model with Gamma family, Gibbs sampler is derived to compute the features for the posterior distribution. By simulation study, the maximum likelihood estimator and the Gibbs estimator are obtained. A numerical study with a simulated data is provided.

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Accelerating Scanline Block Gibbs Sampling Method using GPU (GPU 를 활용한 스캔라인 블록 Gibbs 샘플링 기법의 가속)

  • Zeng, Dongmeng;Kim, Wonsik;Yang, Yong;Park, In Kyu
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.77-78
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    • 2014
  • A new MCMC method for optimization is presented in this paper, which is called the scanline block Gibbs sampler. Due to its slow convergence speed, traditional Markov chain Monte Carlo (MCMC) is not widely used. In contrast to the conventional MCMC method, it is more convenient to parallelize the scanline block Gibbs sampler. Since The main part of the scanline block Gibbs sampler is to calculate message between each edge, in order to accelerate the calculation of messages passing in scanline sampler, it is parallelized in GPU. It is proved that the implementation on GPU is faster than on CPU based on the experiments on the OpenGM2 benchmark.

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Posterior density estimation of Kappa via Gibbs sampler in the beta-binomial model (베타-이항 분포에서 Gibbs sampler를 이용한 평가 일치도의 사후 분포 추정)

  • 엄종석;최일수;안윤기
    • The Korean Journal of Applied Statistics
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    • v.7 no.2
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    • pp.9-19
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    • 1994
  • Beta-binomial model, which is reparametrized in terms of the mean probability $\mu$ of a positive deagnosis and the $\kappa$ of agreement, is widely used in psychology. When $\mu$ is close to 0, inference about $\kappa$ become difficult because likelihood function becomes constant. We consider Bayesian approach in this case. To apply Bayesian analysis, Gibbs sampler is used to overcome difficulties in integration. Marginal posterior density functions are estimated and Bayesian estimates are derived by using Gibbs sampler and compare the results with the one obtained by using numerical integration.

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Convergence Diagnostics for the Gibbs Sampler

  • Sohn, Joong-Kweon;Kim, Heon-Joo;Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.1
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    • pp.1-12
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    • 1996
  • The Gibbs sampler is a substantially powerful tool in Bayesian analysis. However, it is necerssary to choose the numbert of iterations and the size of random samples. This problem has been studied by many researchers. The proposed procedures by them are generally difficult to apply to a practical problem. The attraction of the sampling based approaches is their conceptual simplicity and ease of implementation for users with available computing resources but without numerical analytic efforts. In this paper we consider the problem of determining the number of iterations t, which is simple to application.

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Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.203-210
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    • 2003
  • In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

FUNCTIONAL CENTRAL LIMIT THEOREMS FOR THE GIBBS SAMPLER

  • Lee, Oe-Sook
    • Communications of the Korean Mathematical Society
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    • v.14 no.3
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    • pp.627-633
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    • 1999
  • Let the given distribution $\pi$ have a log-concave density which is proportional to exp(-V(x)) on $R^d$. We consider a Markov chain induced by the method Gibbs sampling having $\pi$ as its in-variant distribution and prove geometric ergodicity and the functional central limit theorem for the process.

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Hierarchical Bayesian Analysis for Stress-Strength Model in Normal Case

  • Lee, In-Suk;Cho, Jang-Sik;Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.11 no.1
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    • pp.127-137
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    • 2000
  • In this paper, we consider hierarchical Bayesian analysis for P(Y < X) using Gibbs sampler, where X and Y are independent normal distributions with unknown means and variances, respectively. Also numerical study using real data is provided.

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Regression Analysis of Doubly censored data using Gibbs Sampler for the Incubation period

  • Yoo Hanna;Lee Jae Won
    • Proceedings of the Korean Statistical Society Conference
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    • pp.237-241
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    • 2004
  • In standard time-to-event or survival analysis, the occurrence times of the event of interest are observed exactly or are right-censored. However in certain situations such as the AIDS data, the incubation period which is the time between HIV infection time and the diagnosis of AIDS is usually doubly censored. That is the HIV infection time Is interval censored and also the time of the diagnosis of AIDS is right censored. In this paper, we Impute the Interval censored infection time using the conditional mean imputation and estimate the coefficient factor of the regression analysis for the incubation period using Gibbs sampler. We applied parametric and semi-parametric methods for the analysis of the Incubation period and compared the results.

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Bayesian estimation of ordered parameters (순서화 모수에 대한 베이지안 추정)

  • 정광모;정윤식
    • The Korean Journal of Applied Statistics
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    • v.9 no.1
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    • pp.153-164
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    • 1996
  • We discussed estimation of parameters using Gibbs sampler under order restriction on the parameters. Two well-knwon probability models, ordered exponential family and binomial distribution, are considered. We derived full conditional distributions(FCD) and also used one-for-one sampling algorithm to sample from the FCD's under order restrictions. Finally through two real data sets we compared three kinds of estimators; isotonic regression estimator, isotonic Bayesian estimator and the estimator using Gibbs sampler.

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