• 제목/요약/키워드: Bayesian

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Bayesian Hypothesis Testing for Two Lognormal Variances with the Bayes Factors

  • Moon, Gyoung-Ae
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.1119-1128
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    • 2005
  • The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor and the fractional Bayes factor have been used to overcome this problem. In this paper, we suggest a Bayesian hypothesis testing based on the intrinsic Bayes factor and the fractional Bayes factor for the comparison of two lognormal variances. Using the proposed two Bayes factors, we demonstrate our results with some examples.

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Bayesian One-Sided Testing for the Ratio of Poisson Means

  • 강상길;김달호;이우동
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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    • pp.295-306
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    • 2006
  • When X and Y have independent Poisson distributions, we develop a Bayesian one-sided testing procedures for the ratio of two Poisson means. We propose the objective Bayesian one-sided testing procedures for the ratio of two Poisson means based on the fractional Bayes factor and the intrinsic Bayes factor. Some real examples are provided.

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Bayesian Testing for the Shape Parameter of Gamma Distribution : An Encompassing Approach

  • Moon, Gyoung-Ae
    • Journal of the Korean Data and Information Science Society
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    • 제16권4호
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    • pp.861-870
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    • 2005
  • The Bayesian model selection procedures for the shape parameter of gamma distribution are proposed in order to test that the failure rate of gamma distribution is constant, increasing or decreasing. The encompassing intrinsic Bayes factor by Beger and Pericchi (1996) based on Jeffreys prior for shape parameter is used to investigate the usefulness of the proposed Bayesian model selection procedures via both real data and pseudo data.

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Bayesian Estimation Using Noninformative Priors in Hierarchical Model

  • Kim, Dal-Ho;Choi, Jin-Kap;Choi, Hee-Jo
    • Journal of the Korean Data and Information Science Society
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    • 제15권4호
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    • pp.1033-1043
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    • 2004
  • We consider the simultaneous Bayesian estimation for the normal means based on different noninformative type hyperpriors in hierarchical model. We provide numerical example using the famous baseball data in Efron and Morris (1975) for illustration.

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Bayesian Test for the Difference of Exponential Guarantee Time Parameters

  • 강상길;김달호;이우동
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2004년도 춘계학술대회
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    • pp.15-23
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    • 2004
  • When X and Y have independent two parameter exponential distributions, we develop a Bayesian testing procedures for the equality of two location parameters. Under the noninformative prior, we propose a Bayesian test procedures for the equality of two location parameters using fractional Bayes factor and intrinsic Bayes factor. Simulation study and some real data examples are provided.

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Bayesian estimation for finite population proportion under selection bias via surrogate samples

  • Choi, Seong Mi;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • 제24권6호
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    • pp.1543-1550
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    • 2013
  • In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

정적계통의 통계적 퍼래미터 추정에 있어 최우도법과 Bayes식방법과의 비교연구 (A Comparative Study Of Maximum Likelihood Method With Bayesian Approach In Statistical Parameter Estimation Of Static Systems)

  • 한만춘;최경삼
    • 전기의세계
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    • 제22권2호
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    • pp.51-56
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    • 1973
  • The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

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베이지안 기법을 이용한 수자원개발 모델 (Water Resources Development Model by Using Bayesian Theory)

  • 김지학;배영주
    • 품질경영학회지
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    • 제19권1호
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    • pp.72-82
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    • 1991
  • This study deals with the problem of water resources development by using bayesian theory. The purpose of this study is to develop the optimal decision model by applying bayesian theory which determine the optimal alternative in water resources development system. A relevant mathematical model to find an optimal solution formulated and then used in developing an efficient water resources that determine optimal alternative. A numerical example is solved to illustrate the algorithm developed.

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A Bayesian Multiple Testing of Detecting Differentially Expressed Genes in Two-sample Comparison Problem

  • Oh Hyun-Sook;Yang Wan-Youn
    • Communications for Statistical Applications and Methods
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    • 제13권1호
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    • pp.39-47
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    • 2006
  • The Bayesian approach to multiple testing procedure for one sample testing problem proposed by Scott and Berger (2003) is extended to two-sample comparison problem in microarray experiments. The prior distribution of each gene's mean for one sample is given conditionally on the corresponding gene's mean for the other sample. Posterior distributions of interesting parameters are derived and estimated based on an importance sampling method. A simulated example is given for illustration.

Bayesian Estimation via the Griddy Gibbs Sampling for the Laplacian Autoregressive Time Series Model

  • Young Sook Son;Sinsup Cho
    • Communications for Statistical Applications and Methods
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    • 제2권2호
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    • pp.115-125
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    • 1995
  • This paper deals with the Bayesian estimation for the NLAR(1) model with Laplacian marginals. Assuming the independent uniform priors for two parameters of the NLAT(1) model, the griddy Gbbs sampler by Ritter and Tanner(1992) is used to obtain the Bayesian estimates. Random numbers generated form the uniform priors ate used as the grids for each parameter. Some simulations are conducted and compared with the maximum likelihood estimation result.

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